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An adaptive fuzzy logic system for residential energy management in smart grid environments

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Abstract

Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects.

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... However, this system lacks adaptation in the thermostat. The system considered in [33] is an extension of [32]. Authors tried to make it adaptive using the same fuzzy logic approach. ...
... • These studies are country oriented research, specifically working with systems in Canada, UK , and others as mentioned in [32] and [33]. These two limitations led us to extend this study for further enhancement in the thermostat setpoints' optimization. ...
... In residential sectors, HVAC systems utilize a large amount of the aggregated energy consumption throughout the world. These systems entail approximately a 64% and about 57% from the aggregated power utilization in Canada and U.S. [33]. They are all comprised on usage of the essential loads which can create peaks or they can contribute to power outages or blackouts due to huge consumption in any particular area (city). ...
Article
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Energy management of residential buildings plays an important role in a smart grid. Region specific fuzzy logic strategies are proposed recently. However, no such approach exists that covers all regions of the world. A fuzzy logic-based strategy for the construction of fuzzy controller covering the entire globe would be cost effective due to the increasing power of micro-controllers. Results show that our proposed approach achieves a minimum energy savings of 6.5%, irrespective of where it is used around the world. This research will provide a model for extending the region specific solutions for a worldwide adaption. INDEX TERMS Energy management, thermostat, smart grid, fuzzy logic, fuzzy inference systems.
... Multiple DSM strategies are utilized to tackle the improper usage of the residential load. There are various scenarios for the irregular use of the electricity in residential sector [16]- [18], [79], [57], [80], [81], [82], [83]- [87], [88]- [90], [91]- [97]. Firstly, there are not sufficient power scheduling mechanisms for handling the consumers' demands (lack of efficient EMCs). ...
... In residential sector, HVAC systems utilize a large amount of the aggregated energy throughout the world. These systems entail approximately 64% and about 57% from the aggregated power utilization in Canada and United States [82]. They are all essential loads which can create peaks or they can contribute to power outages or blackouts due to huge consumption in any particular area (city). ...
... 3) In addition, impact of the delay is not computed [86]. 4) In [82], residential thermostats are automated; however, their work does not consider the thermal comfort of the appliances. In the proposed work, two EMSs are proposed to manage the load of the daily and seasonally used appliances. ...
Thesis
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The transformation of conventional grid into Smart Grid (SG) requires strategic implementation of the demand-sensitive programs while considering the varying fluctuations in the consumers’ load. The core challenges faced by existing electric system are that how to utilize electrical devices, how to tackle large amount of data generated by end devices and how to meet energy demands of consumers in limited resources. This dissertation is focused on the energy management of residential sector in the SG. For this purpose, we have proposed the Energy Management Controllers (EMCs) at three levels: at home level (including the single and multiple homes), at building level and at regional level. In addition, cloud and fog based environments are integrated to provide on-demand services according to the consumers’ demands and are used to tackle the problems in existing electric system. At first level, heuristic algorithms based EMC is developed for the energy management of single and multiple homes in residential sector. Five heuristic algorithms: genetic algorithm, binary particle swarm optimization algorithm, bacterial foraging optimization algorithm, wind driven optimization algorithm and our proposed hybrid genetic wind driven algorithm are used to develop the EMC. These algorithms are used for scheduling of the residential load during peak and off peak hours in a real time pricing environment for minimizing both the electricity cost and peak to average ratio while maximizing the user comfort. In addition, the advancements in the electrical system, smart meters and implementation of Renewable Energy Sources (RESs) have yielded extensive changes to the current power grid for meeting the consumers’ demand. For integrating RESs and Energy Storage System (ESS) in existing EMCs, we have proposed another Home EMC (HEMC) that manages the residential sector’s load. The proposed HEMC is developed using the earliglow algorithm for electricity cost reduction. At second level, a fuzzy logic based approach is proposed and implemented for the hot and cold regions of the world using the world-wide adaptive thermostat for the residential buildings. Results show that the proposed approach achieves a maximum energy savings of 6.5% as compared to the earlier techniques. In addition, two EMCs: binary particle swarm optimization fuzzy mamdani and binary particle swarm optimization fuzzy sugeno are proposed for energy management of daily and seasonally used appliances. The comfort evaluation of these loads is also performed using the Fanger’s Predicted Mean Vote method. For increasing the system automation and on-demand availability of the resources, we have proposed a cloud-fog-based model for intelligent resource management in SG for multiple regions at next level. To implement this model, we have proposed a new hybrid approach of Ant Colony Optimization (ACO) and artificial bee colony known as Hybrid Artificial Bee ACO (HABACO). Moreover, a new Cloud to Fog to Consumer (C2F2C) based framework is also proposed for efficiently managing the resources in the residential buildings. C2F2C is a three layered framework having cloud, fog and consumer layers, which are used for the efficient resource management in six regions of the world. In order to efficiently manage the computation of the large amount of data of the residential consumers, we have also proposed and implemented the deep neuro-fuzzy optimizer. The simulation results of the proposed techniques show that they have outperformed the previous techniques in terms of energy consumption, user comfort, peak to average ratio and cost optimization in the residential sector.
... Different closed control loop strategies have been used in literature for joint demand response management and thermal comfort optimization by considering occupancy schedules [12] weather information, smart zoning [13], and by integrating renewable energy resources [14]. In recent studies, fuzzy logic is used to control the HVAC and lighting system [15], [16]. Fuzzy logic is used to adjust the setpoint of the thermostat for the HVAC system in response to variations in electricity price and energy demand of smart homes [15]. ...
... In recent studies, fuzzy logic is used to control the HVAC and lighting system [15], [16]. Fuzzy logic is used to adjust the setpoint of the thermostat for the HVAC system in response to variations in electricity price and energy demand of smart homes [15]. Likewise, a fuzzy logic-based illuminance controller was proposed to control the illuminance setpoints in response to variation in price, outdoor and indoor illuminance [16]. ...
... However, these proposed controllers are designed to save energy and cost without paying much attention to the comfort evaluation. These control techniques often fix the thermostat setpoint and illuminance setpoint to a single value, consequently, jeopardizing the users' comfort [15], [16]. Therefore, designing control techniques to minimize the tradeoff between energy consumption and user comfort is required. ...
Article
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Internet of things is providing us numerous ways to improve our quality of experience by using smart cyber-physical infrastructure systems. Also, due to arrival of LED lighting systems, there is the possibility to improve user’s visual comfort at less cost. In our proposed model, by using a fuzzy inference system, used in cyber-physical infrastructure system, we save energy from the heating, ventilation and air conditioning system. This saved energy is used to improve the visual comfort of the user. Simulation results show that considering the visual comfort standard of 500 lux instead of 250 lux results in energy savings and ensures visual comfort. Together with the preservation of thermal comfort increases the overall users’ comfort. Since research confirms that users’ improved comfort results in up to 14% of increased productivity. Our model is unique in the sense that using fuzzy logic, indirectly improved the users’ productivity. By using our fuzzy logic controller on electric equipment, we can achieve improved users’ performance without paying any extra cost.
... To overcome the lack of historical data and the computational time challenges of stochastic approaches, fuzzy programming has recently been applied in electricity grid design [21][22][23][24]. A multi-objective robust fuzzy stochastic approach was proposed in Refs. ...
... However, social issues, especially customer-related, have not been properly addressed. In addition, it should be noted that previous studies focused on the design of a SMG [15][16][17][18][19][20][21][22][23][24][25][26] without simultaneously considering the connection with the main grid and the local microgrids to balance power supply and demand. Finally, previous studies focusing on P2P market mechanisms always assumed that microgrids are predetermined. ...
Article
Microgrids with renewable distributed generation units are a promising solution for reducing investment cost and greenhouse gas emissions in the face of rapidly rising energy demand and recent organizational concerns regarding social and environmental issues. However, microgrids closely located to demand areas cannot easily balance power supply and demand owing to the intermittency of renewable energy generation and the barriers to connecting with the main electricity grid. To overcome this problem, this study addresses a sustainable microgrid design problem, where blockchain technology is used for peer-to-peer energy trading in the microgrid. The adoption of the blockchain technology in peer-to-peer energy trading ensures the security and sustainability for participants in the microgrid. It enables the participants to take control of the energy system without the need for a central regulatory authority. The problem entails decisions regarding not only the power flow among entities in the grid and the number, location, and capacity of renewable distributed generation units but also the price mechanism for peer-to-peer trading. A sustainable network microgrid is modeled by considering economic and environmental objectives with social constraints related to maximizing demand satisfaction of consumers. A fuzzy multi-objective programming model is proposed to tackle the variability in the capacity of renewable distributed generation units and demand load. The proposed model is solved by a genetic algorithm. Numerical experiments are conducted to evaluate the feasibility of the proposed model in decision-making regarding microgrid design, the impact of peer-to-peer trading on the total profit of the microgrid, and the applicability of the developed solution approach.
... To understand this concept, it is necessary to remember that tra-ditional grid consists of transmission lines, transformers, and substations, among other components that the provider needs for sending electric power from power generation plants to the end user. Although the main grid is considered a marvel of engineering, currently this approach is over exploited, and it is required another type of grid, one that incorporates the existing technology and overcome current and future issues [6], [7]. ...
... The dynamic control of the MG allows dependence of main grid during normal operation or peak demands, and once the main grid fails, the MG can operate with autonomy. In this situation, the control isolates the fault without affecting its integrity and performance [7], [22], [23]. The case like the above is an example of a power management strategy (PMS), which can help to improve the system's performance and protection. ...
Article
Full-text available
Power management strategies (PMS) are applied to keep a balance, between different energy sources (i.e. solar, wind, geothermal, hydro), storage units (i.e. fuel cell, batteries, fly wheel) and loads. Up to date, there has been reported several advance techniques to solve this task, for instance: Fuzzy Logic, Deep Learning, Droop Control, Bayesian Networks, among others. Nevertheless, some of those PMS are over simplified and others are too complex to be programmed in devices with limited resources. To solve these issues, this paper proposes a PMS based on Fuzzy Logic, which keeps a balance between those two goals. Characteristics of the proposed PMS are a small number of rules; fulfillment of the demanded power at every time; reducing use of the storage unit; and keeping a balance between the different sources, storage unit and loads. The proposed PMS is numerically evaluated by using SIMULINK-MATLAB®, in a 10kW residential DC Microgrid (MG), and validated by using a Hardware in the Loop platform (NI myRio-1900 and Typhoon HIL402). A comparison with three popular advance techniques demonstrates the feasibility of the proposed PMS.
... Moreover, as the membership functions (MFs) are trained offline, it is computationally intensive and can effectively control the battery SOC. The FLC is suitable for uncertain or approximate reasoning; depends on linguistic model; requires no mathematical calculation; and is characterised by smooth controlling, high precision, faster response, and easy implementation (Keshtkar and Arzanpour, 2017). In fact, the input and output MFs of FLC are real variables mapped with linear or nonlinear functions (Keshtkar and Arzanpour, 2017). ...
... The FLC is suitable for uncertain or approximate reasoning; depends on linguistic model; requires no mathematical calculation; and is characterised by smooth controlling, high precision, faster response, and easy implementation (Keshtkar and Arzanpour, 2017). In fact, the input and output MFs of FLC are real variables mapped with linear or nonlinear functions (Keshtkar and Arzanpour, 2017). In Zhang et al. (2017), FLC-based charging-discharging technique is illustrated to maintain the state-of-health of battery, considering the SOC and power. ...
Article
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Please cite this article as: M. Faisal, M.A. Hannan, P.J. Ker et al., Particle swarm optimised fuzzy controller for charging-discharging and scheduling of battery energy storage system in MG applications. Energy Reports (2020), https://doi. Abstract : Aiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging-discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging-discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging-discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging-discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research.
... SHEMS utilizes internet for acquiring the environmental forecast of the next day. It also communicates with utilities to read price signals (Keshtkar & Arzanpour, 2017). ...
... The appliance state can be a single value (ON/OFF) or multistate (Zhai et al., 2018). Within the smart home, the communication technology used in Keshtkar and Arzanpour (2017) are Zigbee or Wifi, based on their performance, however, other technologies can also be utilized. The different communication techniques which can be used within SHEMS are mentioned and compared in Guang et al. (2017). ...
Article
Smart grid is providing new opportunities and techniques for supplying high energy demand of the ever growing energy industry. One-third of the total energy demand comes from the residential sector. A new frontier in this field is the Energy Management Systems being designed for the futuristic smart homes. A smart home is a home that shall be able to decide, control and optimize the operation of its equipments, on its own with minimal interference from its master, a human. One of the major factors for the successful development of a smart home is its ability to manage the energy resources including generation and storage. The recent smart home energy management publications have been reviewed in detail in this paper. The paper also elaborates on different demand response strategies used and the various equipments considered along with renewable energy generation and plug in electric vehicles (EV) employed in smart home energy management process. The literature is categorized based on various factors like tariff, storage, trading, monitoring, etc. affecting the performance of a smart home. These factors are mentioned, discussed and analysed in depth. Objective functions, constraints and communication models involved in smart home energy management models are also surveyed.
... [62,74], are enjoying considerable popularity. Another efficient approach to deal with time-scale discrepancy is to proceed an online dynamic simulation, which has been utilised in several articles [57,76,80], as shown in Figure 7. ...
... W1: This window, which is located in the core layer, continued the path of those works which solved the model in day-ahead, by solving the model in real-time. This idea has been explored in several articles, such as references [62,80,83], which are directed by connection nodes (i.e., large red nodes) to the basic node. W2: As aforementioned, decision making under uncertainty is a critical issue in the operation and control of ABs. ...
Article
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New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.
... Authors used the synergy of wireless sensor networks, fuzzy logic, and smart grid incentives to design a smart thermostat for the HVAC system using a programmable communicating thermostat [83]. Then an adaptive model is developed to adjust the user's changing preferences [84]. The results are compared with the existing thermostat and it is observed that developed systems automatically respond to DR programs and resulted in a significant reduction in load demand without user discomfort [83,84]. ...
... Then an adaptive model is developed to adjust the user's changing preferences [84]. The results are compared with the existing thermostat and it is observed that developed systems automatically respond to DR programs and resulted in a significant reduction in load demand without user discomfort [83,84]. Researchers in [85] proposed a fuzzy logic-based behavioral controller for HEMS. ...
Article
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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.
... Furthermore, with the advent of massive amounts of IEDs and widespread use of communication systems in the grid, two other challenges arise including cyber-security issues and the need for a comprehensive standard to cover data modeling, distributed control, and substation automation [92]. Fuzzy based intelligent relaying has been proposed in many articles which might led to a good options for a reliable protection schemes to micro grid and smart grid as well [93][94][95]. ...
Article
Full-text available
One of the most important domains of concern in electrical power system engineering is the protection and reliability of the system. Nowadays, it is quit evident that the implementation of an intelligent algorithm is paramount in providing protection to the equipment of a substation including auxiliary components using relays and breakers as well. Most of the research have been based on single load connected to a single feeder line. Many different algorithm have been followed to present various intelligent relaying schemes on over current protection, transmission line protection, transformer protection, distance protection to name some. These are based on numerous distinguished algorithm like Digital Signal Processing, Genetic Algorithm, Artificial Neural Network, Fuzzy Algorithm and many more. The aim of this paper is to review articles on Fuzzy based intelligent relaying schemes appearing in scientific journals. We have discussed individual problem statements along with the respective solutions are proposed by the investigator(s) too. Further, we have summarized the present scenario of protection sector of power and energy department along with it's unsolved problem issues. Finally, we identified few of the research gaps to clarify and to suggest future research opportunities.
... A DSM az irányítás válasza, outputja, azokra az inputokra, amiket a fogyasztás és termelés ingadozásai támasztanak. Abból indul ki, hogy ha a termelésre nincs ráhatásunk, akkor a fogyasztás igazításával szintén fenntartható az egyensúly (Palensky and Dietrich, 2011;Maharjan et al., 2016;Keshtkar and Arzanpour, 2017). A DSM megvalósítható a fogyasztók szabályozásával , vagy új fogyasztó bekapcsolásával (Nojavan and Mohammadi-Ivantloo, 2017). ...
... The key benefit of FL is it can be tuned and adapted, therefore enhancing the degree of freedom of MS. FL based EMSs include the conventional FL [66,67],type 2 FL [68], adaptive FL [69][70][71], predictive FL [72] . FL-based EMSs are insensitive to model uncertainties and robust strategies against system disturbances; however, they need an exact design and need larger-speed microcontrollers. ...
Thesis
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This thesis deals with the optimal management of a multi-source energy system for the case of a commercial building. The system under consideration is a grid-connected DC microgrid that includes photovoltaic (PV) panels, fuel cells, and energy storage batteries. Starting from an energy modeling of the building's consumption and integrating uncertainties about consumption and photovoltaic production, the idea is to find technical and economic management strategies that aim to maximize the economic gain while respecting the voltage to be respected at the DC bus level. In this sense, different metaheuristic optimization algorithms (Particle Swarm Optimization (PSO), Bald Eagle search (BES) algorithm, and Salp Swarm Algorithm (SSA))...) are investigated. The results may have shown that these algorithms arrive at solutions whose performance is close with a slight superiority for the BES algorithm. The convergence mechanism of Blade, whose exploration term makes it possible to better manage the local minima, compared to the PSO and SSA algorithms, which translates into better performance. The thesis work on management is extended to propose parameter estimators for batteries and the FC in the perspective of improving the performance of management over the long term.
... Yu et al. [6] proposed a real-time automatic control method that utilizes the Lyapunov optimization tool to minimize the total cost of the HVAC system. Other studies in [7,8] suggested a holistic simulation model for the HVAC system and Keshtkar et al. [9] designed a supervised fuzzy learning method for HVAC to meet the users' preferences with minimal cost. Kusiak et al. [10] introduced a data-driven modeling method to achieve optimal in-home HVAC load scheduling. ...
Preprint
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Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users' privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users' private information locally for solving the primal subproblem hence preserves the users' privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users' peak load and reduce the overall energy cost of the system by 23% on average.
... Furthermore, with the advent of massive amounts of IEDs and widespread use of communication systems in the grid, two other challenges arise including cyber-security issues and the need for a comprehensive standard to cover data modeling, distributed control, and substation automation [92]. Fuzzy based intelligent relaying has been proposed in many articles which might led to a good options for a reliable protection schemes to micro grid and smart grid as well [93][94][95]. ...
Preprint
One of the most important domains of concern in electrical power system engineering is the protection and reliability of the system. Nowadays, it is quit evident that the implementation of an intelligent algorithm is paramount in providing protection to the equipment of a substation including auxiliary components using relays and breakers as well. Most of the research have been based on single load connected to a single feeder line. Many different algorithm have been followed to present various intelligent relaying schemes on over current protection, transmission line protection, transformer protection, distance protection to name some. These are based on numerous distinguished algorithm like Digital Signal Processing, Genetic Algorithm, Artificial Neural Network, Fuzzy Algorithm and many more. The aim of this paper is to review articles on Fuzzy based intelligent relaying schemes appearing in scientific journals. We have discussed individual problem statements along with the respective solutions are proposed by the investigator(s) too. Further, we have summarized the present scenario of protection sector of power and energy department along with it's unsolved problem issues. Finally, we identified few of the research gaps to clarify and to suggest future research opportunities.
... In Ref. 23, FL was employed in the bi-level optimization process of the energy supply system considering cost, energy efficiency, and environment. An adaptive fuzzy logic system was proposed in Ref. 24 for controlling a heating, ventilation, and air conditioning (HVAC) system in a smart grid environment. Fuzzy-based energy management was also proposed for combining the energy storage system and a renewable energy system in the standalone application. ...
Article
Integration between supplies for stationary power and vehicles is potentially useful for increasing the efficiency and the reliability of energy generation systems. Solid oxide fuel cell is one matured technology, which is suitable for a polygeneration system and provides an integration of supply for stationary power and vehicles. However, a combination of solid oxide fuel cell with photovoltaic thermal and thermoelectric generation increases the complexity of a polygeneration system. The system needs a management strategy for dispatching the energies produced. Therefore, in this work, a fuzzy energy management strategy was applied for this polygeneration system by considering two different configurations: an off-grid system with electric vehicle supply and an on-grid system with hydrogen vehicle supply. A two-stage fuzzy energy management strategy considering optimization and management of multi-parameters of the polygeneration components was considered. The evaluation of the optimum fuzzy was analyzed based on energy, economic, and environmental criteria. From the results obtained, the optimal strategy increased the reliability, energy, and system cost savings by 22.05%, 22.4%, and 32.58%, respectively. Moreover, the optimum management reduced the power loss of the polygeneration system by about 48.82%, which was achieved by the configuration with electric vehicles supply and off-grid connection.
... The performance of FLC is more accurate, robust and superior than the PID controller [95], [96]. The fundamental of fuzzy control is based on fuzzy logic theory, in which decisions are made by a set of ''if-then'' statements called the fuzzy rules [62], [97], [98]. These linguistic rules are generally written based on observations made by the controlle's designer and the system operators' knowledge [99], [100]. ...
Article
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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.
... e significant advantage of fuzzy logic over others is that no mathematical modelling is needed for the controller design. e outputs and inputs of a fuzzy controller are mapped with membership functions, and final rules are set to obtain the desired outcome [46]. Also, upon an unexpected change in the system parameters, no modifications are required in the controller, and since the outputs depend on the effects of the inputs, the same rule base can still be used [47]. ...
Article
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Permanent electricity availability should not be taken for granted since grid sustainability and reliability are at stake when there is no balance between supply and demand. This paper employs a load balancing demand-side management (DSM) approach based on fuzzy logic, considering the low energy users who have insignificant influence on system peaks. Through the K-means clustering algorithm, suitable candidates for DSM are identified, and the control mechanism is based on energy utilization and load priority. The results reveal that about 3.7 kW in power saving was achieved per month. This result indicates that, with a proper energy management strategy for an individual customer, almost a flatter load profile and power saving can be achieved.
... This gives to the customer the possibility to program their demand, independently, by taking as reference the instantaneous operating cost delivered by the manager of the microgrid [23]. For this purpose several strategies have already been proven to be effective in load scheduling: the use of fuzzy logic for the optimal management and loads programming in a smart house [24], and many other metaheuristics have allowed moderate consumption planning such as Genetic Algorithm, as proposed in [25], and PSO presented in [26]. Also, Artificial Neural Network algorithm based forecasting model was developed in [27]. ...
Preprint
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Background: The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs. Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.
... Different types of controllers of heating systems may be used to optimize the energy efficiency and to ensure a certain level of comfort for the occupants, these controllers are of type proportional-integral-derivative (PID) controllers, auto-tuning methods of PID parameters [6 , 7] , fuzzy logic controllers [8] , genetic algorithms [9] , distributed iterative learning temperature control [10] , predictive control [11 , 12] and other work such as [13][14][15][16][17][18][19][20] . All these methods require adjustments and are generally based on linear model. ...
Article
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This paper presents a new strategy of supervisory control applied to heating room system that is able to adapt to different operating conditions, in order to reduce the energy consumption and guaranteed a good thermal comfort for the occupants. The aim is to design a multi-model and multi-controller supervisor by using neural networks with a view to overcome the limitations of using a single controller and to deal with nonlinearities, parameters uncertainties and the time constant of the heating system. The proposed methodology is based on three model/controller pairs, a monitoring signal generator and a switching logic. Three architectures of neural network architectures, namely multilayer perceptron neural networks, radial basis function and memory neuron networks are used to evaluate this control technique and to design the different controller/model pairs which correspond to the same work office and a specific isolation state. A comparative study is associated to this work in order to recognize the best neural network in terms of performance and simulation time. The results obtained for three scenarios addressed herein show the importance and the effectiveness of this method.
... Fuzzy logic is a mathematical tool to predict unknown and multifactorial issues (21). The main difference between fuzzy expert systems with conventional expert systems is the presence of numerical processing instead of the symbolic reasoning engines (22). ...
... Previous research includes the use of AI for residential EMS with no prior linkage to a gaming strategy that engages the end-user in the process of energy reduction [39][40][41]. In addition, the previous gamified strategies did not consider personalizing interfaces for energy reduction [42][43][44][45][46] or only proposed frameworks with no interface proposals [47,48]. ...
Article
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Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.
... Fuzzy set theory, causal symmetry as discussed by Woodside (2013), looks into the relationship of predictors by the means of values and latent variables characterized by high and low values for sufficiency and predicting variables as they occur. Causal symmetry consists of more than one complex combination of antecedents and requires not just variables but also causal recipes to complete an analysis (Keshtkar & Arzanpour, 2017). Fuzzy set results can be classed as incomplete or incorrect causal if the casual symmetry is not applied during analysis. ...
Article
The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society.
... Complementary and equifinality have similar underlying assumptions regarding patterns of attributes that demonstrate different features and results depending on the constructs relationship structures [78]. Contextually, attributes within a relationship were arranged by condition (present or absent) and connected as standalone items for analyzing the result, rather than the overall effect of all attributes. ...
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The increasing use of digital technologies has significantly reshaped marketing and consumer behavior (CB) as online communities and cutting-edge innovations such as artificial intelligence (AI) disrupt and advance consumer attitudes on specific products and services. As such, online communities that are supported by AI technologies creating new knowledge from consumer interactions through platforms like social media as consumers share experiences on specific products or services. Since AI is designed to “learn” and improve with data generated from digital technologies linked to consumer interactions, AI relies on consumer knowledge-sharing (KS) activities to replicate new knowledge for product and service improvement. However, given the knowledge gap in this area, this article applies the fsQCA technique to data generated from 291 participants to develop CB metaframework predicted on the concepts of AI, CB, and KS. Our results suggest that AI advances consumer attitudes and behaviors when knowledge is acquired while online communities promote curiosity and engage consumers to learn by sharing experiences about specific products or services. Furthermore, understanding the causality between AI, CB, and KS concepts offers critical decision-making insights to marketing experts across the industry.
... A DSM az irányítás válasza, outputja, azokra az inputokra, amiket a fogyasztás és termelés ingadozásai támasztanak. Abból indul ki, hogy ha a termelésre nincs ráhatásunk, akkor a fogyasztás igazításával szintén fenntartható az egyensúly (Palensky and Dietrich, 2011;Maharjan et al., 2016;Keshtkar and Arzanpour, 2017). A DSM megvalósítható a fogyasztók szabályozásával , vagy új fogyasztó bekapcsolásával (Nojavan and Mohammadi-Ivantloo, 2017). ...
... Heating, Ventilation and Air Conditioning (HVAC) structures signify a major section of entire household power utilization. Programmatic thermoregulators are operated largely for self-regulating restriction of household HVAC structures [1] . The main target of smart grid initiatives is to support end users to decrease their power utilization throughout immense power requirement. ...
Preprint
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In our research work, we have implemented interval type 2 fuzzy logic model (IT2FL) applied to Smart grids in Wireless Sensor Networks (WSNs). The smart grids equipped with our technique are proficient to mainly react to various metrics like effective electricity cost and heat to continue end user’s thermic relaxation. Moreover the capabilities of WSNs to identify, examine and calculate various variables have been assessed to progress the restrictions of actual power control organizations for instance thermoregulators. We have taken Set-point weight, Time-Weights and Override flag as input to evaluate System output in weights and minutes. Efficacy of the recommended framework is made by means of statistical analysis and multiple linear regressions(MLR)
... AI addresses the integration of the management systems, either horizontally extending the supervision to smart building clusters, smart districts or smart cities, or vertically following any hierarchy implementation, like ISA-95 Standard. One of the works about energy devices is the research [77], which defines an 'adaptable smart thermostat' for residential energy management, utilizing fuzzy logic and wireless sensors capabilities, in order to avoid that residential customers must manually re-program their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. The thermostats must autonomously learn and adapt to users' schedule and preference changes in order to save energy. ...
Article
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Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of “Autonomous Cycles of Data Analysis Tasks”, which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.
... The FLC has been adopted for the identification of real-time driving patterns of an electric vehicle (EV), and combined with the dynamic programming to realize self-adaptive energy management [8]. Moreover, using FLC, an autonomous thermostat has been designed for residential energy management [9]. Considering the grid frequency and the state of charge (SOC), the charging or discharging of EVs has been adjusted by an FLC [10]. ...
... A fuzzy logic-based smart thermostat has been developed by Keshtkar and Arzanpour (2017) for home HVAC systems. It will adjust the accuracy based on the set-point and consider the outdoor temperature, preference of the resident, and the price of electricity. ...
Article
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Demand-side response plays an essential role in the residential energy management system (REMS). The mixed-integer linear programming (MILP) performs day-ahead load scheduling that includes consumer satisfaction, incorporates a wide variety of home appliances and energy storage systems, distributes energy resources. However, the comfort level achieved by a smart thermostat and the price elasticity of residents’ electricity demand is not considered in pricing-based demand-side response (DSR). To overcome these research gaps, the internet of things (IoT)-based REMS (IoT-REMS)—including DSR—is introduced in this study. A multi-objective optimization problem is built with various constraints for interruptible and non-interruptible appliances, energy storage systems, solar energy systems, and electric vehicles. Using the developed model for various home appliances, IoT-REMS provides reliable solutions with different user satisfaction levels. The co-ordinated scheduling used in the IoT-REMS minimizes the daily energy consumption by shifting the load from on-peak to medium-peak and off-peak duration. A case study in five different houses with and without IoT-REMS was carried out, and a performance comparison has also been made. The results of IoT-REMS show the effectiveness of the proposed method and that it can be applied to any other type of smart home.
... A peak hour energy demand solution for an HVAC residential system was presented in [100]. Two methods were derived in this paper. ...
Article
Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.
... A peak hour energy demand solution for an HVAC residential system was presented in [100]. Two methods were derived in this paper. ...
Article
Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.
... For example, Tsao and Thanh [8] proposed a fuzzy multi-objective programming model (FMOPM) to handle demand load in the presence of the variability of renewable distributed generation units. Keshtkar and Arzanpour [24] developed a fuzzy logic model for the detection, learning and adaptation of new user's requirements. The results showed that the proposed model had the ability to learn and adapt to the uncertainty of new user's requirements. ...
Article
The intermittent nature of renewable sources, uncertain demand load, and the location of microgrids cause challenges for the proper energy balance between supply and demand. To overcome the issues caused by such challenges and to be economically efficient while minimizing environmental impacts, microgrid design with renewable energy sources has recently attracted the attention of researchers. However, the sustainable microgrid design involving multiple types of demand areas (residential and industrial areas) and seasonal factors has not been explored by researchers. Therefore, this paper investigates the sustainable microgrid design problem with multiple types of demand areas and peer-to-peer energy trading involving seasonal factors and uncertainties to maximize total profit and to minimize environmental costs while satisfying demand. The problem is to determine optimal decisions on the number, location, and capacity of renewable distributed generation sources, energy flows, and seasonal electricity sales prices for the system that includes peer-to-peer trading. A fuzzy multi-objective programming model is developed to handle the uncertainties of demand load and capacity of renewable distributed generation sources. A genetic algorithm is applied to solve the proposed model. Results of computational experiments to assess its effectiveness and efficiency show that the proposed model increases total profit by over 12% and reduces environmental costs by almost 30% compared to the cases of no peer-to-peer energy trading and seasonal factors.
... To overcome these drawbacks, several intelligent MPPT techniques (PSO, fuzzy, adaptive fuzzy, ANN, RBFNN, etc.) are proposed in the literature [10][11][12][13][14]. The superlative performance of these trackers maximizes the extraction of power during varying weather conditions. ...
Article
In the present paper, adaptive fuzzy maximum power point tracking is employed as a means to generate the corresponding gating signals of an interleaved soft-switching boost converter (ISSBC) connected to a photovoltaic system. It is common knowledge that the output characteristics of a photovoltaic cell are nonlinear and its output power fluctuates largely with variations in radiation and temperature; thus, effectively its efficiency is very low, i.e., below 50%. The efficiency is improved by using ISSBC, and simultaneously, losses due to switching are minimized by the adoption of a resonant (soft) switching technique. The next concern lies with this being a tracking MPP, which has been efficiently achieved by incorporating adaptive fuzzy MPPT which is better as compared to the published articles. The proposed MPPT increases the system efficiency as well as drastically reduces the cost of fabrication. A 1.2 kW prototype of the above-defined model is considered for analysis. Determination of the viability of each mode of operation of the aforementioned photovoltaic power generation system is assessed meticulously in the MATLAB 2017 software. In addition to simulation, digital simulation in real time (Opal-RT) is performed in the laboratory to measure the accuracy and potentiality of the respective model verifying the results.
... Ø An autonomous and flexible energy management approach for residential microgrids is introduced in [22]. To improve the fuzzy logic controller efficiency, the proposed solution considers different parameters and information of buildings that directly concern energy management and thermal comfort. ...
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In this paper, a microgrid based on wind and solar generation resources for a standalone application is studied. The unpredictable nature of renewable energy resources causes certain control problems within an isolated microgrid. In addition, the variable load consumption complicates the control task and affects the quality of power and voltage. However, power quality and energy management are crucial tasks under unpredictable wind and load conditions in off-grid applications. Indeed, integrating an efficient energy management system can offer many technical advantages regarding microgrid performance and autonomy. This paper presents an advanced energy management system based on a fuzzy logic controller to enhance power quality and load supply while maintaining battery life. Extracting the maximum power from energy sources and supply quality voltage to the consumers are also investigated. The main contribution lies in testing the isolated microgrid stability and robustness against short-circuit on the load and generation side. Thereafter, several scenarios demonstrating the microgrid response given the most common faults are considered. The obtained results highlight the efficiency and high performance of the proposed energy management strategy in both faulty and healthy conditions. Furthermore, the findings comply with the international standards IEEE 1547 and ICE 617227.
... The advancement of computer science has made solutions to cope with uncertainty problems available. Current solutions for uncertainty problems mostly focus on technical approaches, such as fuzzy sets [66] and stochastic optimization [67]. By contrast, this study applied the social-science perspective and developed a management mechanism that demonstrates how humans and computers cooperate. ...
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Artificial intelligence (AI) has been applied to various decision-making tasks. However, scholars have yet to comprehend how computers can integrate decision making with uncertainty management. Obtaining such comprehension would enable scholars to deliver sustainable AI decision-making applications that adapt to the changing world. This research examines uncertainties in AI-enabled decision-making applications and some approaches for managing various types of uncertainty. By referring to studies on uncertainty in decision making, this research describes three dimensions of uncertainty, namely informational, environmental and intentional. To understand how to manage uncertainty in AI-enabled decision-making applications, the authors conduct a literature review using content analysis with practical approaches. According to the analysis results, a mechanism related to those practical approaches is proposed for managing diverse types of uncertainty in AI-enabled decision making.
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A smart home requires energy management systems to operate optimally. It has an integrated renewable energy source along with a storage system. The output of the renewable energy source cannot be controlled. The ESS is optimally scheduled for charging and discharging in order to derive the maximum utilization of it. Optimal scheduling of battery integrated smart home is proposed in this paper to minimize the energy costs for a given set of daily electricity tariffs scheme. The sizing of the battery installed in a prosumer system is investigated. An SHEMS is modelled, and an appropriate objective function is defined to reflect the operational costs of a prosumer. The battery efficiency is related to the applied tariff for deciding the operational characteristics of the storage system. The optimal control of the battery operation is then proposed considering its constraints. The sensitivity analysis is performed on the battery to observe its performance in different conditions. The results show the effectiveness of the proposed algorithm to reduce the operational costs of the smart home.
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This paper presents a techno-economic feasibility analysis related to a heat pump installation in a poly-generative energy district to convert the overproduction of electricity into thermal power, easy to be stored in thermal storage tanks. The heat pump technology is already used for thermal/cooling energy production in different areas although application in energy districts in a power-to-heat modality to improve management of electrical/thermal energy demands is still limited. In this research, the installation of a heat pump in the poly-generative smart grid located at the University of Genoa Campus is presented. A time dependent one-year techno-economic analysis of the energy district is performed, throughout a model built with a software developed by the authors. The integration of the heat pump in the energy district is analysed, comparing the energetic, environmental and economic performance to the present configuration of the poly-generative energy district. The results show that the heat pump introduction grants several advantages, such as a reduction in gas consumption (24 ton/year, -15%) and an increase in the annual energy efficiency of cogenerative prime movers which can work for a higher number of hours (+23%) close to the design point.
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Recently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer’s high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances’ energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization.
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Smart thermostats and home energy management systems (HEMSs) are generally studied separately. However, their joint use can provide a greater benefit. Therefore, this study primarily aims to combine a smart thermostat with a HEMS. The mixed-integer linear programming (MILP)-based HEMS performs day-ahead load scheduling for cost-minimization and provides optimal demand response (DR) and photovoltaic (PV) self-consumption, and the fuzzy logic-based thermostat aims efficient DR of air-conditioning and maintenance of thermal comfort. In the first stage, unlike conventional fixed set-point thermostats, the proposed thermostat defines different set-points for each time interval, by fuzzifying input parameters of electricity prices, solar radiation, and occupant presence, to be used by HEMS. In the second stage, the HEMS schedules the operation of time-shiftable, thermostatically controlled, and power-shiftable (battery energy storage system (BESS), electric vehicle (EV)) loads. The HEMS considers bi-directional power flow between home, BESS, EV, and grid, as well as battery degradation to avoid unnecessary energy arbitrage. The simulation results show that a daily cost reduction of 53.2 % is achieved under time-of-use (TOU) and feed-in tariff rates of Turkey. AC cost is reduced by 24 % compared to conventional thermostats. In a future scenario of real-time pricing (RTP) and dynamic feed-in tariff, vehicle-to-grid (V2G) becomes possible.
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This article experimentally demonstrates a novel, microgrid control algorithm based on a two-layer economic model predictive control framework that was previously developed by the authors. This algorithm is applied to an isolated microgrid with a solar photovoltaic system, a battery bank and a gasoline-fuelled generator. The control system performance is experimentally compared to a baseline algorithm over 5 min and 10 h periods, while an experimentally validated model is used to compare performance over a full year. The results indicate that applying the proposed, two-layer economic model predictive control algorithm can reduce operating costs and CO2 emissions by 5%–10% relative to conventional, rule based methods, and by 10%–15% if improved solar and demand forecasts are available. Furthermore, the proposed two-level algorithm can achieve reductions of up to 5% compared with current state-of-the-art methods which only attempt to optimize performance in the energy management system.
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Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users’ privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users’ private information locally for solving the primal subproblem hence preserves the users’ privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users’ peak load and reduce the overall energy cost of the system by 23% on average.
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The main aim of this paper is to design and test new software application to support experts involved in decision-making in the field of energy security. The prototype named ESecFuzzy is designed for this purpose. It implements various fuzzy concepts (fuzzy rules, fuzzified input data, fuzzy variables utilized by fuzzy terms and related fuzzy sets) chosen to obtain reasoning in case of unclear, uncertain or even incomplete input data for reaching a conclusion about them. The research was conducted on a sample of 28 European Union countries over a ten-year period (2008–2017). The research outcomes are as follows: (a) ESecFuzzy application for energy security measurement was developed; (b) application testing; (c) measuring energy security on a given sample, and (d) evaluation of the accuracy and acceptability of fuzzy logic as an approach for energy security quantification. ESecFuzzy provides the experimenting with different input data (time series intersected with geospatial data) as well as fine-tuning of fuzzy rules in knowledge base. Through the evaluation and experimenting process, ESecFuzzy application demonstrated the advantage of fuzzy logic by obtaining the derived information based on mass of heterogeneous data organized in time series that indicate general trends in the target application domain.
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RESUMO: A busca por novas metodologias que conduzam a melhores desempenhos acadêmicos é uma preocupação constante nos órgãos públicos e nas instituições de ensino. Essas metodologias devem ser capazes de extrair informações dos ambientes externos e internos ao ambiente de ensino e possibilitar a identificação de obstáculos e estratégias que contribuam para o desenvolvimento e aprimoramento dos cursos ofertados. Neste estudo, apresentamos o desenvolvimento de uma ferramenta inteligente baseada em Lógica Fuzzy, que poderá auxiliar o docente a predizer os resultados esperados de seus alunos. O algoritmo desenvolvido utiliza as Redes de Petri coloridas como base de modelagem e utiliza diversos parâmetros de entrada que considera informações imprecisas, permitindo a utilização de variáveis linguísticas, tais como: baixo desempenho, regular participação em sala, alto desempenho para obter uma avaliação mais precisa do discente. Essa abordagem permite a análise e avaliação de situações que seriam desconsideradas em métodos de avaliação clássicos.
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Multi-dimensional stochastic factors challenge the interactive energy scheduling of the industrial integrated energy system (IIES). Previous research focuses on either deterministic energy scheduling or individual stochastic scheduling while neglecting complicated interactions among uncertain parties, which brings the research gaps about stochastic multi-party’s interaction. In this regard, a multi-party stochastic energy scheduling approach in IIES is proposed based on the stochastic game. A decentralized decision support system is considered, and a stochastic utility model is designed for decentralized IUs with multi-dimensional stochastic factors from photovoltaic (PV) production and IIES parameters, enabling them to participate in the multi-energy scheduling with their own strategies. A stochastic game model is developed considering the thermoelectric coupling and the IUs’ interaction. The co-decision mechanism, recognizing different transfer times of electrical and thermal energy, is built based on the state transition within the game. Moreover, a distributed solution algorithm that includes the Markov decision process and iterative method is designed to address the problem of the “curse of dimensionality” arising from multiple stochastic factors. Finally, case studies with realistic data from an industrial park in Guangdong Province, China, are designed to show the effectiveness of the proposed approach, which enhances IUs’ profits by 9.4% and fits flexible load strategies and price strategies. The decentralized system can also reduce the computation time by 70.1% compared to the centralized system. Through analyzing different number of scenarios and intervals for PV generation, electrical and thermal load, the conclusion has obtained that increase the number of scenarios has a negative effect on IUs’ decision, but increase the number of load intervals contributes to more specific results and higher utility.
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Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.
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With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.
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The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
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Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users’ privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users’ private information locally for solving the primal subproblem hence preserves the users’ privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users’ peak load and reduce the overall energy cost of the system by 23% on average.
Conference Paper
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Residential Heating, Ventilation, and Air Conditioning (HVAC) systems can play significant role in the future smart grids in order to balance demand and supply patterns as they are the main electrical load during peak load periods. Programmable thermostats and programmable communicating thermostats are widely used for automatic control of residential HVAC systems with the aim of energy management and providing thermal comfort while users set their daily/weekly schedules and preferences. On the other hand, the programs such as Time-of-Use (TOU) rates, Real-time Pricing (RTP), and Demand Response (DR) are often applied by utilities in order to encourage users to reduce their consumption during peak load periods. However, it is often an inconvenience for residential users to manually modify their schedules and preferences based on the electricity prices that vary over time. Hence, in this paper an autonomous thermostat capable of responding to different parameters such as time-varying prices, while saving energy and maintaining user's thermal comfort is presented. The developed thermostat is the result of integration of fuzzy logic, wireless sensors, and smart grid initiatives. To implement and validate the approach; a house simulator that represents a smart thermostat is developed in Matlab-GUI. The simulation results demonstrate the overall improvement with respect to energy saving and conservation without jeopardizing occupant's thermal comfort.
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Occupant behavior is nowadays acknowledged as a main source of discrepancy between predicted and actual building performance; therefore, researchers attempt to model occupants’ presence and adaptive actions more realistically. Literature shows a proliferation of increasingly complex, data-based models that well fit the cases analyzed. However, the actual use of these models by practitioners is very limited. Moreover, simpler models might be preferable, depending on the aim of investigation. The present study proposes shifting the focus to fit-for-purpose modeling, in which the most appropriate model for a specific case is characterized by the lowest complexity, while preserving its validity with respect to the aim of the simulation. A number of steps are taken to achieve this shift in focus. The existing models are presented according to complexity. The available inter-comparison studies are critically reviewed. Subsequently, a list of parameters that affect the choice of an appropriate modeling strategy is presented as a first attempt to derive guidelines and generate a framework for investigation. To support such claims the effect of some of the listed parameters is evaluated in a case study. The main conclusion to be drawn is that determining the best complexity for occupant behavior modeling is strongly case specific.
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So-called ‘smart thermostats’ are beginning to fill the gap left in efficiency programs after researchers and policy makers discovered that in practice, simple programmable thermostats do not guarantee energy savings. As a result, EPA ended EnergyStar certification of programmable thermostats in 2010. Many recent pilots for communicating thermostats, occupancy-responsive thermostats, and adaptive control schemes have shown significant annual HVAC savings on the order of 10-20%. However, the form and function for technologies in this space vary widely. Some controls merely allow for remote management (e.g., web-based set-point scheduling or smart-phone interface and control), while other devices monitor occupancy and automatically adjust set-points when a space is vacant. Still other technologies automatically adapt to user behaviors and preferences in order to anticipate changes and adjust HVAC operation. These differences have different savings implications. Further, the application into which any of these technologies is installed also impacts savings potential. The study focuses particularly on a series of pilot evaluations conducted with one occupancy-responsive adaptive thermostat system that resulted in very little energy savings during normal operation in university residence halls. These results came as a great surprise to the research team, especially since the HVAC system run-time for vacant zones was reduced to nearly zero in the buildings. The detailed evaluation of this case forms a conceptual basis for explanation of the limitations for smart thermostat devices. The research shows that considerable savings can be had in certain instances, but that the impact is sensitive to technology and application. The study also reviews previous research on the technology and recommends methodological improvements for future studies.
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This paper presents a novel air-conditioning system with proactive demand response to smart grid. The system consists of a chilled water storage system (CWS) and a temperature and humidity independent control (THIC) air-conditioning system. Using this system, building power demand can be flexibly controlled as desired by implementing two demand response strategies: demand side bidding (DSB) strategy and demand as frequency controlled reserve (DFR) strategy, in respond to the day-ahead and hour-ahead power balance requirements of the grid, respectively. Considerable benefits can be achieved for both power companies and end-users under incentive pricing mechanisms. A case study concerning on the demand response performance of the proposed system is also conducted in an office building.
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A fuzzy test for testing statistical hypotheses about an imprecise parameter is proposed for the case when the available data are also imprecise. The proposed method is based on the relationship between the acceptance region of statistical tests at level ? and confidence intervals for the parameter of interest at confidence level 1 ? ?. First, a fuzzy confidence interval is constructed for the fuzzy parameter of interest. Then, using such a fuzzy confidence interval, a fuzzy test function is constructed. The obtained fuzzy test, contrary to the classical approach, leads not to a binary decision (i.e. to reject or to accept the given null hypothesis) but to a fuzzy decision showing the degrees of acceptability of the null and alternative hypotheses. Numerical examples are given to demonstrate the theoretical results, and show the possible applications in testing hypotheses based on fuzzy observations.
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With the recent initiatives to upgrade the existing power grid to the Smart Grid (SG), there has been a significant interest in the design and development of an efficient communications infrastructure for connecting different components of the SG. In addition to the currently used underlying networks and protocols, new wired/wireless approaches are being planned for deployment for different components/applications of the SG. Based on the data requirements of the applications, new challenges have arisen at the network layer of the protocol stack with respect to routing and data forwarding. In this paper, we focus on the routing issues in the SG communications infrastructure which consists of different network components, such as Home Area Networks (HANs), Neighborhood Area Networks (NANs) and Wide Area Networks (WANs). We provide a comprehensive survey of the existing routing research and analyze the advantages and disadvantages of the proposed protocols with respect different applications areas. We also identify the future research issues that are yet to be addressed with respect to the applications and network components. This survey is the first to identify routing design issues for the SG and categorize the proposed routing protocols from the SG applications perspective. We believe that this work will be valuable for the utilities and other energy companies whose target is to develop and deploy a specific SG application that may span different network components. In addition, this work will provide valuable insights for the newcomers who would like to pursue routing related research in the SG domain.
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This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions.As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.
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This report summarizes results of a literature review, a workshop, and many meetings with demand response and thermostat researchers and implementers. The information obtained from these resources was used to identify key issues of thermostat performance from both energy savings and peak demand perspectives. A research plan was developed to address these issues and activities have already begun to pursue the research agenda.
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Restructuring of power markets has helped in the penetration of distributed generation (DG) in the electricity networks. Microgrids are low voltage distribution networks comprising various distributed generators (DG), storage devices and controllable loads that can operate interconnected or isolated from the main distribution grid, as a controlled entity. This paper describes the main functions of the microgrid central controller required for the optimization of microgrid operation its interconnected operation. This is achieved by maximizing its value, i.e. optimizing production of the local DGs and power exchanges with the main distribution grid
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The incentives such as demand response (DR) programs, time-of-use (TOU) and real-time pricing (RTP) are applied by utilities to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. In this paper, a fuzzy logic approach (FLA) utilizing wireless sensors and smart grid incentives for load reduction in residential HVAC systems is presented. Programmable communicating thermostats (PCTs) are used to control residential HVAC systems in order to manage and reduce energy use, while consumers accommodate their everyday schedules. Hence, the FLA is embedded into existing PCTs to augment more intelligence to them for load reduction, while maintaining thermal comfort. To emulate an actual thermostat, a PCT capable of handling both TOU and RTP is simulated in Matlab/GUI. It is utilized as a ‘simulator engine’ to evaluate the performance of FLA via applying several different scenarios. The results show that the FLA decreases/increases the initialized set points without jeopardizing thermal comfort by applying specific fuzzy rules through evaluating the information received from wireless sensors and smart grid incentives. Our approach results in better energy and cost saving in residential buildings versus existing PCT.
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This paper presents a statistical method for modeling the behavior of household occupants to estimate residential energy consumption. Using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS), actions carried out by survey respondents are categorized into ten distinct activities. These activities are defined to correspond to the major energy consuming loads commonly found within the residential sector. Next, time varying minute resolution Markov chain based statistical models of different occupant types are developed. Using these behavioral models, individual occupants are simulated to show how an occupant interacts with the major residential energy consuming loads throughout the day. From these simulations, the minimum number of occupants, and consequently the minimum number of multiple occupant households, needing to be simulated to produce a statistically accurate representation of aggregate residential behavior can be determined. Finally, future work will involve the use of these occupant models along side residential load models to produce a high-resolution energy consumption profile and estimate the potential for demand response from residential loads.
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Managing peak demand efficiently is vital for maintaining uninterrupted supply of electrical power by utility providers. In this work, a pilot system was developed for managing and controlling the demand of major power consuming equipment in buildings from a central server, while relying mostly on existing infrastructure and maintaining consumer comfort. The system was successfully demonstrated on a selected group of buildings using the LonWorks networking platform. At the building level, the system utilized power line and twisted pair communication to control the thermostats of air-conditioning (A/C) units. The higher level communication was executed through extensible markup language (XML) and simple object access protocol (SOAP). The system provided control capabilities based on A/C unit priority, thermostat temperature, building type and geographic location. The development and execution of demand management strategies for selected buildings led to peak load reductions up to 74%, in addition to energy savings up to 25%. Implementing such a system at a national level in Kuwait is estimated to reduce peak demand by 3.44 GW, amounting to capital savings of $4.13 billion. The use of existing infrastructure reduced the cost and installation time of the system. Based on the successful testing of this pilot system, a larger-scale system is being developed.
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Most residential heating, ventilating, and air-conditioning (HVAC) systems utilize a single zone for conditioning air throughout the entire house. While inexpensive, these systems lead to wide temperature distributions and inefficient cooling due to the difference in thermal loads in different rooms. The end result is additional cost to the end user because the house is over conditioned. To reduce the total amount of energy used in a home and to increase occupant comfort there is a need for a better control system using multiple temperature zones. Typical multi-zone systems are costly and require extensive infrastructure to function. Recent advances in wireless sensor networks (WSNs) have enabled a low cost drop-in wireless vent register control system. The register control system is controlled by a master controller unit, which collects sensor data from a distributed wireless sensor network. Each sensor node samples local settings (occupancy, light, humidity and temperature) and reports the data back to the master control unit. The master control unit compiles the incoming data and then actuates the vent resisters to control the airflow throughout the house. The control system also utilizes a smart thermostat with a movable set point to enable the user to define their given comfort levels. The new system can reduce the run time of the HVAC system and thus decreasing the amount of energy used and increasing the comfort of the home occupations.
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In discussions on smart grids, it is often stated that residential end-users will play a more active role in the management of electric power supply and demand. They are expected to shift from a passive role as consumer of electricity to an active role as co-provider. In this article, the extent to which current technologies, products and services empower end-users to take up an active role as co-providers is evaluated. Based on a review of literature and related pilot projects, current approaches are driven by technical and financial considerations. There appears to be a lack of product and service design that supports end-users in their role as co-providers. This is reflected in the lack of thought given to how the end-users’ process of behavioral change can be supported to enable the transition from consumer to co-provider. Several recommendations are provided for product and service designers towards fostering the role of co-provider, which comes under: (a) user interaction needs, (b) approaches to behavioral change and (c) community initiatives and management of resources. Designers are considered to play a bridging role between policy making and engineering, whilst facilitating involvement of end-users in the design process.
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In energy and environment field models are constructed, in general, based on well-defined physical phenomena and properties. Calibration and uncertainty analysis hold a particular interest because models represent a simplification of reality and, therefore, it is necessary to quantify to what degree they are imperfect before employing them in design, prediction and decision making processes. Integrated building energy models attempt to describe the effect of various internal and external actions (weather, occupancy, appliances, etc.) through physical relations (both algebraic and differential) and they are being widely used to design and operate high performance buildings, which are an essential component of a global energy strategy to reduce carbon emission and fossil sources depletion. An approach oriented to systems and able to integrate effectively field measured data and computer simulations for calibration in the modeling process has the potential to revolutionize the way buildings are designed and operated, and to stimulate also the development of new technologies and solutions in the field. The research presented in this paper aims to represent an initial step towards this integrated approach.
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This paper provides an insight into consumer engagement in smart grid projects in Europe. Projects analysed are those included in the catalogue annexed in the JRC Report “Smart Grid projects in Europe: lessons learned and current developments”. The analysis suggests an increase in the interest in consumer engagement projects at European level and a strong focus on the residential sector, and emphasises the key importance of public funding to support these projects. The study also reveals that projects involving consumers are characterised by the pursuit of two main objectives: gaining deeper knowledge of consumer behaviour (observing and understanding the consumer) and motivating and empowering consumers to become active energy customers (engaging the consumer). The paper reviews the main activities undertaken to obtain these objectives and highlights trends and developments in the field. Finally, the paper discusses obstacles to consumer engagement and the strategies adopted by the projects surveyed to tackle them, highlighting the need to build consumer trust and to design targeted campaigns taking into consideration different consumer segments. The conclusions are in line with findings and analyses presented in the literature and underscore the need for further research and action at European level.
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The paper addresses the problem of controlling a Heating Ventilation and Air Conditioning (HVAC) system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most conditions are conflicting goals requiring an optimisation method to find appropriate solutions over time. A discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and maintain thermal comfort; and the optimisation method, a discrete branch and bound approach. Each component will be described, with special emphasis on a fast and accurate computation of the PMV indices. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer and winter conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.
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Indoor thermal comfort is the most commonly studied type of comfort in the literature. We can find works which try to predict the user's satisfaction or keep static conditions by means of black-box controllers. However, we propose a novel system which is capable of adapting to the user's thermal preferences without any prior knowledge, and measuring his comfort level by aggregating several thermal parameters into one single thermal index. This single value is used in a static set of fuzzy rules easily understood by the user, and the labels used in those rules are dynamically adapted to the estimated preferences of the user. Experimental simulation shows that our proposal is capable of learning on-line the optimal thermal feeling for the user, and an-ticipating the necessary actions to obtain such thermal comfort in an indoor environment. The ubiquitous nature of nodes in a wireless sensor networks (WSN) opens up wide possibilities for combining its distributed sensing power with ad-vanced adaptive real-time learning systems. Thus, this paper also presents a proposal of integration of a thermal comfort adaptive fuzzy system into Coral2K R ⃝, a centralized platform for monitoring and control heterogeneous WSN networks.
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Since the energy crisis of 2000-2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a change out of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40% may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, this paper surveys the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. It finds conclusive evidence that households respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. In addition, the design of the studies, the tools used to analyze the data and the geography of the assessment influence demand response. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between 3 and 6% and critical-peak pricing (CPP) tariffs induce a drop in peak demand that ranges between 13 and 20%. When accompanied with enabling technologies, the latter set of tariffs lead to a reduction in peak demand in the 27-44% range.
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This paper considers joint problems of control and communication in wireless sensor and actuator networks (WSANs) for building-environment control systems. In traditional control systems, centralized control (CC) and distributed control (DC) are two major approaches. However, little work has been done in comparing the two approaches in joint problems of control and communication, particularly in WSANs serving as components of control loops. In this paper, we develop a CC scheme in which control decisions are made based on global information and a DC scheme which enables distributed actuators to make control decisions locally. We also develop methods that enable wireless communications among system devices compatible with the control strategies, and propose a method for reducing packet-loss rate. We compare the two schemes using simulations in many aspects. Simulation results show that the DC can achieve a comparable control performance of the CC, while the DC is more robust against packet loss and has lower computational complexity than the CC. Furthermore, the DC has shorter actuation latency than the CC under certain conditions.
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Electric utilities in the United States and globally are heavily investing to upgrade their antiquated delivery, pricing, and service networks including investments in the following areas: -- smart grid, which generally includes improvements upward of the meters all the way to the transmission network and beyond -- smart metering, sometimes called advanced metering infrastructure (AMI), which usually includes control and monitoring of devices and appliances inside customer premises -- smart pricing including real-time pricing (RTP) or, more broadly, time-variable pricing, sometimes including differentiated pricing -- smart devices and in-home energy management systems such as programmable controllable thermostats (PCTs) capable of making intelligent decisions based on smart prices -- peak load curtailment, demand-side management (DSM), and demand response (DR) -- distributed generation, which allows customers to be net buyers or sellers of electricity at different times and with different tariffs, for example, plug-in hybrid electric vehicles (PHEVs), which can be charged under differentiated prices during off-peak hours. The main drivers of change include: -- insufficient central generation capacity planned to meet the growing demand coupled with the increasing costs of traditional supply-side options -- rising price of primary fuels including oil, natural gas, and coal -- increased concerns about global climate change associated with conventional means of power generation -- demand for higher power quality in the digital age.
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Thermal comfort is very important in any work or operation environment. But “thermal comfort” is a very vague and not easily defined term, and it is influenced by both the physical environment and the individual’s physiology or psychology. To at least partially overcome these problems, this work proposes the use of a fuzzy adaptive network (FAN) to model the thermal comfort system. To illustrate the approach, actual experimental data were used to train the network and to give results. Although only very simple examples were used, the results show the usefulness of the proposed approach.
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Thermal comfort and use of thermostats in homes and office rooms were examined by a quantitative interview survey with a nationally representative sample in Finland. The total number of respondents was 3094. The results show that thermal comfort levels are lower in offices than in homes. People feel cold and hot more often in offices than in homes during both the winter and summer seasons. The perceived control over room temperature is remarkably low in offices. Higher thermal comfort levels and perceived control in homes are supported by greater adaptive opportunities. In offices people have fewer opportunities to control the thermal environment, people deal worse with thermostats, and people have lower opportunities to adapt to different thermal environments.
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