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Energy Efficient Buildings based on Occupants Behaviour: A survey
Abstract
Residential buildings consume significant energy and play a key role in increasing demand all over the world. The major sources of energy consumption in buildings are heating, ventilation and air conditioning (HVAC) system, lighting and plug loads. The concerns have been raised over balancing the demand and supply, occupants comfort and rapid depletion of energy resources. Therefore, an efficient utilization of energy is an essential component in buildings to cope with these challenges. In this work, an extensive survey is carried out on state-of-the-art work for occupants comfort management and efficient energy utilization. The main areas under consideration include indoor environmental comfort, energy optimization, computational algorithms , control strategies, energy consumption sectors, energy sources and simulation tools. Index Terms-Energy Efficient Buildings, Smart Grid, Smart Buildings I. BACKGROUND With an exponential increase in utilization of energy resources , the world's primary energy resources are at the brink of depletion. Buildings sector consume up to 40% of total energy in developed countries [1] and responsible for 30% of CO 2 emissions. Heating, ventilation and air conditioning (HVAC) systems are the major sources of energy consumption in buildings. The emissions of CO 2 , the dearth of energy resources and economic concerns demanding an efficient utilization of buildings' energy with an optimal control of occupants' comfort. People usually spend about 90% of their time in buildings. The primary objective of building energy and comfort management (BECM) system is to achieve and maintain occupants' indoor environmental comfort while alleviating the use of energy at the time of building operations. Indoor comfort is the key component in buildings so as to preserve occupants' health, productivity, working efficiency and gratification. Generally, indoor environmental comfort parameters: thermal, visual, plug loads, air quality and humidity are considered in BECM systems. Thermal comfort is a state of mind in which occupants' satisfaction with respect to thermal environment is expressed [2]. The transfer of heat between human body and its surrounding significantly affects the degree of thermal comfort. Visual Comfort is the measure of maintaining desired illuminance level inside a specific zone. Whereas, plug loads are the end-use energy utilizing devices other than HVAC and lightning loads [3]. Pollution-free air and presence of water vapours in indoor environment are also the metrics of occupants' comfort. Authors in [4] investigated dynamic and adaptive models for energy optimization. Besides dynamicity, consumers' satisfaction is also analyzed. Results of the survey demonstrate that 40% of energy can be saved for both HVAC and lightening control loads. Moreover, significant attention is given to user preferred control as well as occupancy based control. By utilizing the modern information and communication technologies , the proposed model is more reliable and computationally efficient. In [5], indoor environmental comfort parameters of building along with energy conservation are reviewed at large scale. Computational algorithms are widely demonstrated so as to optimize the performance of buildings. Moreover, various control mechanisms and simulations tools are discussed in detail. II. OPTIMIZATION OBJECTIVES Authors in [6] mainly focus on the operation of HVAC, as it directly affects consumers productivity and indirectly affects energy conservation. The main purpose of the proposed work is to optimize the energy consumption and occupants' thermal comfort. In [7], Christos et al. developed a concept of grid connected microgrids which mainly depend on photovoltaic source. The ultimate objective of the presented model is the minimization of energy consumption cost and thermal comfort of consumers. The cost is minimized by utilizing maximum renewable energy and minimum grid energy, moreover optimal control of thermal load based on occupancy pattern reduces significant energy consumption cost. A model comprises of peak demand reduction and short-term energy prediction is presented in [8]. The minimization of peak energy consumption is achieved by maintaining peak load with in a predefined limit. Moreover, users activities are analysed so as to forecast short-term power demand. The optimization of thermal comfort is considered as a primary objective function in [9], as thermal comfort is a core component while describing the operation of HVAC system. The proposed paradigm aims to learn user thermal comfort profile, so as to control the HVAC operation by providing and maintaining the occupants thermal preferences. Besides maximizing the thermal comfort, energy conservation is also achieved. Authors of [10], [11] presented their models with an objective of maximizing users satisfaction while keeping the
... The development of energy-efficient buildings can benefit from technical improvements, smart design, and the use of appropriate materials [5], but also from the monitoring and motivated change of occupants [6]. Various data fusion strategies have been proposed in the literature [7], and their objectives range from monitoring device • A smart sensor fusion solution that uniquely combines actual readings from multiple sensors with usage patterns from past sensors' data, and user feedback in order to generate personalized action recommendations. ...
Despite the variety of sensors that can be used in a smart home or office setup, for monitoring energy consumption and assisting users to save energy, their usefulness is limited when they are not properly integrated into the daily activities of humans. Energy-saving applications in such environments can benefit from the use of sensors and actuators when data are properly fused with previous knowledge about user habits and feedback about current user preferences. In this article, we present an online recommender system implemented in the EM3 platform, a platform for Consumer Engagement Toward Energy-Saving Behavior. The recommender system uniquely fuses sensors’ data with user habits and user feedback and provides personalized recommendations for energy efficiency at the right moment. The user response to the recommendations directly triggers actuators that perform energy-saving actions and is recorded and processed for refining future recommendations. The EM3 recommendation engine continuously evaluates the three inputs (i.e. sensor data, user habits, user feedback) and identifies the micro-moments that maximize the need for the recommended action and thus the recommendation acceptance. We evaluate the efficiency of the proposed recommender system, which is based on a stacked-LSTM for fusing multi-sensor data streams, in several scenarios, and the observed accuracy on predicting the right moment to send a recommendation to the user ranged from 93% to 97%.
Improving the energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial to reduce buildings’ energy costs and their carbon footprint. HVAC systems are complex, large-scale structures with pure lag time and high thermal inertia. Although traditionally, physical-based methods have been used to model, control and optimise them, data-driven approaches have demonstrated to be more application relevant, easier to compute and better suited to handle nonlinearities. Based only on measured or estimated data, data-driven approaches are highly dependent on the quality of the used data. In recent years, the advances in Information and Communication Technology (ICT), decreasing hardware cost, and improving data accessibility, have allowed the collection and storage of a large amount of high-quality building-related data, allowing the development of more accurate and robust data-driven approaches, making them gain great popularity in HVAC applications. In this paper, a Systematic Literature Review (SLR) based on a database search is conducted to give an in-depth insight into the major challenges regarding modelling, controlling and optimising HVAC systems, making the especial focus on the capability of data-driven models to improve their energy performance while keeping the users’ comfort. The main results of the SLR highlight the importance of taking users’ needs into account when modelling, controlling and optimising HVAC systems to avoid their underutilisation. In particular, the increasing tendency to include users’ feedback into Model Predictive Control (MPC) loops and use easy-to-access technologies, such as WiFi and Smartphone Applications (Apps), to acquire users’ information suggests promising future research horizons.
Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Integration of renewable energy sources in microgrids can be achieved via demand response programs, which change the electric usage in response to changes in the availability and price of electricity over time. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in microgrids equipped with renewable energy sources and energy storage units. The proposed work aims at covering two main gaps in current state-of-the-art demand response programs. The first gap is integrating the objective of matching energy generation and consumption with the occupant behavior and with the objective of guaranteeing thermal comfort of the occupants. The second gap is developing a scalable and robust demand response program. Large-scale nature of the optimization problem and robustness are achieved via a two-level supervisory closed-loop feedback strategy: at the lower level, each building of the microgrid employs a local closed-loop feedback controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the local controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. The effectiveness of the proposed method is validated in a microgrid composed of three buildings, a photovoltaic array, a wind turbine, and an energy storage unit. Comparisons with alternative demand response strategies reveal that the proposed strategy efficiently integrates the renewable sources; energy costs are reduced and at the same time thermal comfort of the occupants is guaranteed. Furthermore, robustness is proved via consistent improvements achieved under heterogeneous conditions (different occupancy schedules and different weather conditions).
Nowadays with the emerging of smart micro-grids(SM-Gs) in residential sectors, a large portion of energy consumption can be saved through optimal scheduling of household devices and management of domestic hybrid energy sources. By the aid of such technologies, residential consumers have the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. This paper presents a novel residential energy management system (REMS) to improve the efficiency of energy consumption in a typical SM-G taking into account minimum cost of energy as well as maximum user's comfort level as competitive objectives. The optimization model is also formulated as a mixed integer nonlinear problem (MINLP) and its performance is tested under different operating scenarios with real data. The simulation results show that the proposed model not only reduces energy consumption costs, but also ensures a comfortable lifestyle for occupants.
Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies.
h i g h l i g h t s DSM scheme that could maximize satisfaction within user's budget is presented. Load-satisfaction algorithm using the scheme is developed based on GA. We quantified user satisfaction based on certain rules. We develop cost per unit satisfaction index, k($) that relates budget to satisfaction. The proposed algorithm is effective in offering the maximum satisfaction. a b s t r a c t This paper presents a demand side load management technique that is capable of controlling loads within the residential building in such a way that the user satisfaction is maximized at minimum cost. Load-satisfaction algorithm was developed based on three postulations that allow satisfaction to be quantified. The input data required by the algorithm includes the power ratings of the electrical devices, its time of use, kW h electrical consumption as well as the satisfaction of the user on each electrical appliance at every hour of the day. From the data, the algorithm is able to generate an energy usage pattern, which would give the user maximum satisfaction at a predetermined user-budget. A cost per unit satisfaction index (k) which relates the expenditure of a user to the satisfaction achievable is also derived. To test the applicability of the proposed load-satisfaction management technique, three budget scenarios of $0.25/day, $0.5/day and $1.00/day are performed. The result of each of the scenarios using the proposed techniques is compared to cases in which the electrical appliances are randomly used. The results obtained revealed that the proposed algorithm offered the maximum satisfaction and minimum cost per unit satisfaction for all the scenarios.
Programmable Communicating Thermostats (PCTs) as price-responsive thermostats are being used broadly for automatic control of residential HVAC systems in North America. The main advantage of existing PCTs is their capability to receive pricing signals from smart meters and shed HVAC load during high demand. In these cases; PCT automatically reduces the initialized set points to the levels pre-adjusted by user. However, there still exist neglected potentials for demand-side management that resides in the control and interaction of PCTs. Hence, it requires making PCTs more intelligence to learn and adapt to user’s preference changes which results in adaptive demand-side management. In this paper, an algorithm which is based on the integration of rule-based techniques and wireless sensors capabilities is presented. The proposed algorithm is embedded into exiting PCTs to improve their capabilities in learning and adapting to occupant’s pattern changes. The simulation results demonstrate that PCTs equipped with our approach performs better than existing PCTs with respect to energy saving and adapting to occupant’s pattern changes. To verify the feasibility of the algorithm; an embedded system using ZigBee wireless communication is built and applied to a conventional air conditioning (AC) system. The experimental results show the system precisely adapts to user’s preference changes while saving energy and cost.
Implementing demand response (DR) in commercial buildings can play a major role in reducing building’s peak load. This improves the efficiency of electricity grids and mitigates expensive peak demand/energy charges for buildings. Due to the lack of Energy Management Systems, small and medium-sized commercial buildings have not historically played much role as a DR resource. This paper presents an integrated control of major loads in commercial buildings, i.e., cooling, lighting and plug loads that can maintain occupant environmental preferences. Each zone’s space temperature set points are optimally adjusted to maintain thermal comfort. Lighting levels, with and without daylight availability, are tightly controlled to maintain desired illuminance levels. Unlike other studies, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-min resolution data set at the individual plug load level. The research evaluates total building performance including interdependencies between lighting, plug load, HVAC and control systems interacting in a realistic manner, both among themselves and with building occupants. In this paper, a method to determine the DR potential of a building, i.e., the amount of electrical demand (kW) by load type that can be shifted or shed, is discussed.
For human-centered automation, this study presents a wireless sensor network using predicted mean vote (PMV) as a thermal comfort index around occupants in buildings. The network automatically controls air conditioning by means of changing temperature settings in air conditioners. Interior devices of air conditioners thus do not have to be replaced. An adaptive neurofuzzy inference system and a particle swarm algorithm are adopted for solving a nonlinear multivariable inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV models, the particle swarm algorithm is more accurate than ANFIS according to computational results. Based on the comfort temperature, this study utilizes feedforward-feedback control and digital self-tuning control, respectively, to satisfy thermal comfort. The control methods are validated by experimental results. Compared with conventional fixed temperature settings, the present control methods effectively maintain the PMV value within the range of ± 0.5 and energy is saved more than 30% in this study.
We present an adaptive control strategy for lighting control in office spaces, with the aim to reduce energy consumption and provide occupant comfort. Based on the premise that each occupant is unique, yet consistent in his actions, the set-points for switching the artificial lights on and off are derived dynamically from statistical analysis of the occupancy sensor data, and from interaction with the occupant. We present a 6-week case study in 10 offices, where we achieved 37.9% and 73.2% energy savings compared to a standard setting control baseline and a worst-case scenario, respectively. We show that individual offices determine individual set-points and discuss their adaptive nature.
In France, non-residential buildings account for a significant part of energy consumption. A large part of this consumption is due to HVAC (Heating, Ventilation and Air-Conditioning) systems, which are in most cases poorly handled. The present work deals with an efficient approach allowing energy consumption to be minimized while still ensuring thermal comfort. We propose a predictive control strategy for existing zoned HVAC systems and consider the PMV (Predicted Mean Vote) index as a thermal comfort indicator. In order to test this strategy, we modelled a non-residential building located in Perpignan (south of France) using the EnergyPlus software. The twofold aim is to limit the times during which the HVAC sub-systems are turned on and to ensure a satisfactory thermal comfort when people are working in the considered building. This predictive approach, computationally tractable, allows thermal comfort requirements to be met without wasting energy.
The aim to maintain thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of an HVAC (heating, ventilation and air conditioning) system. This problem is being widely analyzed, since it has a direct effect on users’ productivity, and an indirect effect on energy saving. This paper presents a hierarchical thermal comfort control system with two layers. The upper layer includes a non-linear model predictive controller that allows to obtain a high thermal comfort level by optimizing the use of an HVAC system in order to reduce, as much as possible, the energy consumption. On the other hand, the lower layer is formed by a PID (proportional, integrative and derivative) controller with anti-windup function which is in charge of reach the setpoints calculated by the non-linear model predictive controller. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a bioclimatic building are included and commented.
Occupant presence and behaviour in buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energy-unaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity.
Energy and comfort management is the major task for a building automation system. As a trend of next-generation's commercial buildings, intelligent buildings are capable of facilitating intelligent control of the building to fulfill occupants’ needs. Since occupants’ behaviors have a direct impact on the system performance, the building should be able to interact with occupants by responding to their requests and obtaining feedbacks based on their behaviors. In this paper, a multi-agent based intelligent control system is developed for achieving effective energy and comfort management in a building environment. The developed multi-agent system turns out to be capable of facilitating the building to interact with its occupants for realizing user-centered control of buildings.
In this paper, we present a novel intelligent coordinator control system based on hierarchical structure. The presented structure consists of one coordinator and five fuzzy logic controllers. The intelligent coordinator architecture contains uses a master and a slave agent. The master agent is an economy behavior fuzzy system and the slave agent consists of two fuzzy negotiation machines (FNMs) and one decision logic unit. Type-1 FS models are chosen to characterize the concepts of thermal comfort, illuminance and carbon dioxide concentration. The word “comfort” is represented by a 3D fuzzy set in fuzzy cube. Using the symmetric fuzzy equality measure we can measure the membership grade of the 3D fuzzy comfort set. We present the structure of a fuzzy controller-agent (FCA) and propose the tuning of parameters of the FLC by genetic algorithms (GAs). The simulation results show that the proposed intelligent control system successfully manages the users’ preferences for thermal and illuminance comfort, indoor air quality and the energy conservation.
Model predictive control (MPC) allows the integration of weather forecasts and of the expected building thermal behavior into the energy management system of buildings. The MPC algorithm requires an accurate but also computationally efficient mathematical model of the building thermal behavior. In this paper, several lumped-parameter thermal models of a passive house with an integrated photovoltaic system are compared to evaluate the model complexity needed to capture the basic thermal behavior of the entire building. In order to reduce implementation costs, the state and parameters of the finally chosen 1R1C model are estimated online with an extended Kalman filter (EKF). In addition, this self-adaptive thermal model provides online estimations of the unmeasured heat flows caused by the inhabitants. The results show that the EKF yields a robust convergence of the parameters after approximately three weeks and that the adapted model is able to generate a prediction of the heat demand for several days. The predicted reference room temperature shows average deviations of less than 1 degrees C for two-day predictions and of less than 3 degrees C for four-day predictions. Therefore, the proposed self-adaptive thermal building model is well suited to be used in a MPC environment.
Increased awareness on environmental issues, together with requirements imposed by implementation of energy efficiency codes, has generated the need for tools that evaluate thermal performance of buildings. Their objective is to ensure compliance and certification. However, existing computer models are still rudimentary with many limitations for use in early design stages of any architectural project. They require exact data in a stage when designers consider conceptual ideas from a range of options rather than precise details and numbers. Design tools that suggest solutions based on ideas are rare. This disadvantage can be seen in the planning of intelligent facades, where the number of possible configurations can be overwhelming and decisions made in early stages have profound effects on energy and comfort performance.This paper presents NewFacades, a model that helps pass from ideas to significant concepts in the design of intelligent facades. It uses energy and visual comfort strategies abstracted from a prescriptive energy code for hot climates to suggest a range of good starting solutions. Designers can have energy and visual comfort estimations to these alternatives through an advanced stage energy simulation program such as EnergyPlus, or explore them further with other tools.Methods used by the tool are presented here in the context of sample cases. By working on energy principles or relying on local existing energy standards, the model can be extended to all types of climates.
User activity recognition for energy saving in smart homes
- P Cottone
- S Gaglio
- G L Re
- M Ortolani
Cottone, P., Gaglio, S., Re, G.L. and Ortolani, M., 2015. User activity
recognition for energy saving in smart homes. Pervasive and Mobile
Computing, 16, pp.156-170.