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User Comfort Enhancement in Home Energy Management Systems using Fuzzy Logic
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Energy consumption in residential sector is the 25% of all the sectors. Maintaining user comfort and energy optimization are the major tasks of Home Energy Management System. Appliances of Heating, Ventilation and Air Conditioning (HVAC) and lighting devices constitute up to 64% and 4% of energy consumption respectively in residential buildings. Different techniques like Demand Response (DR) and pricing tariffs like Time Of Use (TOU) has been used to make user participate in the energy consumption reduction. In the literature review, many techniques have been discussed which use Fuzzy Logic System integrated with other techniques for energy consumption minimization. However, user comfort is often sacrificed in these techniques.
In this thesis, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an input parameter in order to maintain the thermostat set-point according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption simulation. When defining FIS, number of rules in the rule base plays an important role in the correct working. With the increase in number of rules, task of defining them in FIS becomes time consuming and chances
of manual errors increase. In this research, we have also proposed the automatic rule base generation using combinatorial method. Proposed techniques
are evaluated using Mamdani FIS and Sugeno FIS. The input parameters of
proposed FIS are indoor temperature, outdoor temperature, occupancy, price
rate, initialized set points and humidity whereas energy consumption is the
output of the system. Performance metrics used for the evaluation of the
MATLAB simulation results are energy consumption, Peak-to-Average Ratio (PAR), cost, efficiency gain and user comfort. In addition, a model has
been proposed which will quantify the user comfort with respect to different energy consumption levels. Simulation result validates that proposed
technique reduces energy consumption by 28%.
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... It can make the household microgrid beneficial to not only the HEMS users but also the public utilities [110]. There are six different strategies of load profiling which are peak shaving, valley filling, strategic conservation, load shifting, strategic growth, and flexible load shape [13], [98], [111], [112] Compared with the first three strategies, load shifting, strategic growth, and flexible load shape provide more systematic and large-scale changes in load management. Table 6 represents the objective function of the load profiling, which mainly includes load peak minimization [98], [113], peak-to-average ratio (PAR) reduction [91], [103], [114], self-consumption [106] and energy balance [115]. ...
Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies’ power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.
Pakistan is facing severe energy crisis in spite of the fact that nature has blessed her with huge energy potential. Short fall of electricity supply in the country is increasing and has been recorded up to 4522 MW in 2010. This deficit reached to 7000 MW in May, 2011. A comprehensive review of Pakistan's energy sector is presented in this paper. Energy potential, major issues of energy sector and energy import options are discussed. Issues like poor management, combined cycle capacity, low hydro power share, circular debt and energy security have been covered. Energy potential assessment includes hydro solar, wind, coal, nuclear, hydrogen cells, geo-thermal, ocean resources and bio mass. Future prediction calculations are based upon country's current and world's average per capita energy consumption. Current oil and gas reserves of the country contribute to only 5 percent and 48.8 percent of the energy mix and at the current rate will be exhausted by 13 and 16 years respectively. The overwhelming dependence of the energy sector on imported fossil fuels may create a situation of energy security threat. However dependence upon the energy import options cannot be avoided in order to lessen the severity of energy crisis in near future. Some of the energy import options are: Turkmanistan, Afghanistan, Pakistan and India (TAPI); Iran, Pakistan and India (IPI) gas pipelines; Liquefied Natural Gas (LNG) from Qatar etc. On the other hand exploitation of vast renewable potential like hydro, solar and wind requires serious attention. Exploitation of indigenous coal resources would also be a key for solving energy crisis in the long run. In summary, this paper presents energy potential assessment in context of major issues, future predictions and impact of energy import options. This in turn provides a big, clear and brighter picture of the country's energy sector.
In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE)and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes(TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.
Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in Underwater Wireless Sensor Networks (UWSNs). In this paper, we present a Balanced Load Distribution (BLOAD) scheme to avoid energy holes created due to unbalanced energy consumption in UWSNs. Our proposed scheme prolongs the stability period and lifetime of the UWSNs. In BLOAD scheme, data (generated plus received) of underwater sensor nodes is divided into fractions. The transmission range of each sensor node is logically adjusted for evenly distributing the data fractions among the next hop neighbor nodes. Another distinct feature of BLOAD scheme is that each sensor node in the network sends a fraction of data directly to the sink by adjusting its transmission range and continuously reports data to the sink till its death even if an energy hole is created in its next hop region. We implement the BLOAD scheme, by varying the fractions of data using adjustable transmission ranges in homogeneous and heterogeneous simulation environments. Simulation results show that the BLOAD scheme outperforms the selected existing schemes in terms of stability period and network lifetime.
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.
Controlling power utilization in the residential area is one of the major challenges in the smart grid (SG). Demand response (DR) has played a vital role in energy management and improved it with the involvement of residential consumers who participate in such programs from utilities for scheduling their appliances to the off peak hours. In this paper, we have proposed a worldwide adaptive thermostat model for effectively managing the power in all countries of the world. The proposed approach has been evaluated with the help of fuzzy logic and its two inference systems (FIS): 1) Mamdani and 2) Takagi Sugeno. Utilizing the membership functions; outdoor temperature, user occupancy, utility price and initialized setpoints are evaluated for maintaining the buildings temperature. Furthermore, energy consumption in buildings is analyzed by tuning the indoor initialized setpoints while considering all the aforementioned parameters. Simulations are conducted in Matlab to validate the proposed system model and results show that energy consumption in cold countries is reduced upto 45% as compared to the existing programmable approach. Index Terms—Energy management, thermostat, smart grid, fuzzy logic, takagi sugeno fuzzy inference system, mamdani fuzzy inference system
Pakistan is facing multiple challenges for harnessing the indigenous energy resources and devise rational energy policies. The country is believed to have abundant energy resources, however, coping substantial electricity supply gap of over 5000 MW. This paper analyses country's energy and power planning studies conducted since its independence in 1947 and policies announced so far. It is found that water resources management attained more emphasis in early decades of post-independence rather than energy concerns. The first energy and power planning study was conducted in late 1960s and since then various studies were undertaken to supplement five yearly medium term development plans of government. However, it is pertinent to mention that formal energy and power policies were only announced from 1994 onwards owing to growing electricity demand and progressing industrialization. Beside this, the focus of these policies is not only varied but were conceived without undertaking integrated energy planning using energy modeling tools e.g. MARKAL/TIMES; LEAP, ENPEP BALANCE, MESSAGE and EnergyPLAN. It is despite the fact that these tools are successfully applied globally for devising the energy policies and address the complexities of energy system by assisting effective policy formulation. This study recommends that integrated energy planning using energy modeling tools will be helpful to develop sustainable energy policies in Pakistan to eradicate electricity crises.
Thermal comfort is a complex topic and the methods studied so far are only approximate. Many investigators are likely to face some of the issues addressed in this article. The focus of the review is on selected issues and trends relevant to thermal comfort. Meta-analysis was performed using the ASHRAE RPA-884 database. The aim is to address some of the methodological issues of preliminary data analysis and predictions of comfort temperatures. An examination of how to assess the age factor for thermal comfort consideration in the ASHRAE database was also conducted by using an explicit transparent methodology. A new procedure was developed for predicting and analysing comfort temperature. The suggested procedure goes beyond the obvious need for more research studies to underscore deficiencies in data collection, which will lead to better data analysis. Editors might use the results and recommendations from this investigation before publishing original research on thermal comfort.
Keywords
Thermal comfort; Review; Methodology; Meta-analysis; ASHRAE RP-884
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.
This paper reviews occupancy based Model Predictive Control (MPC) for building indoor climate control. Occupancy behavior in buildings is stochastic and complex in nature. With better understanding of occupancy presence in rooms and spaces, advanced controls, such as MPC, can be designed to achieve a more energy efficient operation, compared to more traditional control methods, while comfort is maintained. This paper starts with an overview of traditional controls implemented in buildings, and importance of occupancy based controls. Various control-oriented building modeling methods including physics-based and data-driven models are reviewed. Later on, a comprehensive review of MPC in terms of control theory, objective functions, constrains, optimization methods, system characteristics and various types of MPC is presented conducted. In principle, MPC finds an optimal sequence of control commands to optimize an objective function, considering system model, disturbances, predictions and actuation constraints. Lastly, occupancy based controls including commonly used rule-based and latest model-based controls are reviewed. In addition, a few experimental studies are presented and discussed. The paper presents a holistic overview of occupancy-based MPC for building heating, ventilation, and air conditioning (HVAC) systems, and discusses current status and future challenges. The purpose of this paper is to provide a guideline forresearchers who would like to conduct similar studies to have a better understanding of established research methods.