Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review

Different factors such as thermal comfort, humidity, air quality, and noise have significant combined effects on the acceptability and quality of the activities performed by the building occupants who spend most of their times indoors. Among the factors cited, thermal comfort, which contributes to the human well-being because of its connection with the thermoregulation of the human body. Therefore, the creation of thermally comfortable and energy efficient environments is of great importance in the design of the buildings and hence the heating, ventilation and air-conditioning systems. Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior. This systematic literature review aims to provide an overview of the intelligent control strategies inside building and to investigate their ability to balance thermal comfort and energy efficiency optimization in indoor environments. Methods. A systematic literature review examined the peer-reviewed research works using ACM Digital Library, Scopus, Google Scholar, IEEE Xplore (IEOL), Web of Science, and Science Direct (SDOL), besides other sources from manual search. With the following string terms: thermal comfort, comfort temperature, preferred temperature, intelligent control, advanced control, artificial intelligence, computational intelligence, building, indoors, and built environment. Inclusion criteria were: English, studies monitoring, mainly, human thermal comfort in buildings and energy efficiency simultaneously based on control strategies using the intelligent approaches. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used. Initially, 1,077 articles were yielded, and 120 ultimately met inclusion criteria and were reviewed.

[1]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[2]  G. Lachiver,et al.  A fuzzy control system based on the human sensation of thermal comfort , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[3]  Jin Wen,et al.  INCLUDING OCCUPANTS IN BUILDING PERFORMANCE SIMULATION: INTEGRATION OF AN AGENT-BASED OCCUPANT BEHAVIOR ALGORITHM WITH ENERGYPLUS , 2014 .

[4]  E. W. Shaw.  Thermal Comfort: analysis and applications in environmental engineering, by P. O. Fanger. 244 pp. DANISH TECHNICAL PRESS. Copenhagen, Denmark, 1970. Danish Kr. 76, 50 , 1972 .

[5]  K. Dalamagkidisa,et al.  Reinforcement learning for energy conservation and comfort in buildings , 2007 .

[6]  新 雅夫,et al.  ASHRAE(American Society of Heating,Refrigerating and Air-Conditioning Engineers)大会"国際年"行事に参加して , 1975 .

[7]  K. Kalaitzakisc,et al.  Implementation of an integrated indoor environment and energy management system , 2004 .

[8]  Mohsen A. Jafari,et al.  Prediction of building indoor temperature response in variable air volume systems , 2020 .

[9]  Abbas Javed,et al.  Modelling and optimization of residential heating system using random neural networks , 2014, 2014 IEEE International Conference on Control Science and Systems Engineering.

[10]  S. Ari,et al.  Constrained fuzzy logic approximation for indoor comfort and energy optimization , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[11]  Xiwang Li,et al.  An operation optimization and decision framework for a building cluster with distributed energy systems , 2016 .

[12]  Elias B. Kosmatopoulos,et al.  A low-complexity control mechanism targeting smart thermostats , 2017 .

[13]  Abbas Javed,et al.  Experimental testing of a random neural network smart controller using a single zone test chamber , 2015, IET Networks.

[14]  Jin Woo Moon,et al.  Improving sustainability of ever-changing building spaces affected by users’ fickle taste: A focus on human comfort and energy use , 2020 .

[15]  Navid Delgarm,et al.  Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC) , 2016 .

[16]  Yuan He,et al.  Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques , 2020, Comput. Commun..

[17]  Jin Woo Moon,et al.  Comparative performance analysis of the artificial-intelligence-based thermal control algorithms for the double-skin building , 2015 .

[18]  Servet Soyguder,et al.  Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system , 2009, Expert Syst. Appl..

[19]  Yuekuan Zhou,et al.  Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities , 2020 .

[20]  Paulo Carreira,et al.  Can HVAC really learn from users? A simulation-based study on the effectiveness of voting for comfort and energy use optimization , 2018, Sustainable Cities and Society.

[21]  Ruxu Du,et al.  Thermal comfort control based on neural network for HVAC application , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[22]  Milind Tambe,et al.  Coordinating occupant behavior for building energy and comfort management using multi-agent systems , 2012 .

[23]  Hao Huang,et al.  A hybrid model predictive control scheme for energy and cost savings in commercial buildings: Simulation and experiment , 2015, 2015 American Control Conference (ACC).

[24]  Srinivasan Keshav,et al.  Optimal Personal Comfort Management Using SPOT+ , 2013, BuildSys@SenSys.

[25]  Timothy Reissman,et al.  Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences , 2020, Sustainability.

[26]  Jonghoon Ahn,et al.  Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment , 2020 .

[27]  J. S. Savier,et al.  Intelligent control system for efficient energy management in commercial buildings , 2013, 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy.

[28]  R. Neves-Silva,et al.  Distributed model predictive control for thermal house comfort with auction of available energy , 2012, 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP).

[29]  António E. Ruano,et al.  An Intelligent Weather Station , 2015, Sensors.

[30]  Li Xia,et al.  A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[31]  Ludovic Favre,et al.  Convergence of Multi-Criteria Optimization of a Building Energetic Resources by Genetic Algorithm , 2018 .

[32]  Yang Gao,et al.  Using data mining in optimisation of building energy consumption and thermal comfort management , 2010, The 2nd International Conference on Software Engineering and Data Mining.

[33]  Wei Feng,et al.  A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost , 2020, Energy.

[34]  Kecheng Liu,et al.  An Epistemic-Deontic-Axiologic (EDA) agent-based Energy Management System in office buildings , 2017 .

[35]  William J. Batty,et al.  Fuzzy control strategies to provide cost and energy efficient high quality indoor environments in buildings with high occupant densities , 2003 .

[36]  Abbas Javed,et al.  Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC , 2017, IEEE Internet of Things Journal.

[37]  Jin Woo Moon,et al.  Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings , 2016 .

[38]  Weiwei Liu,et al.  A neural network evaluation model for individual thermal comfort , 2007 .

[39]  Gerardo Maria Mauro,et al.  Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort , 2016 .

[40]  Madeleine Gibescu,et al.  Enabling Cooperative Behavior for Building Demand Response Based on Extended Joint Action Learning , 2018, IEEE Transactions on Industrial Informatics.

[41]  Jin Woo Moon,et al.  Comparative study of artificial intelligence-based building thermal control methods – Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network , 2011 .

[42]  David E. Culler,et al.  Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control , 2012, Proceedings of the IEEE.

[43]  Joon-Ho Choi,et al.  Development of a Data-Driven Approach for Human-Based Environmental Control , 2017 .

[44]  J. Ioannidis,et al.  The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration , 2009, Annals of Internal Medicine [serial online].

[45]  Zoltán Nagy,et al.  Using machine learning techniques for occupancy-prediction-based cooling control in office buildings , 2018 .

[46]  Yimin Chen,et al.  Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Model , 2019, e-Energy.

[47]  António E. Ruano,et al.  Neural networks based predictive control for thermal comfort and energy savings in public buildings , 2012 .

[48]  Stephen Snow,et al.  A Comfort-Based Approach to Smart Heating and Air Conditioning , 2017, ACM Trans. Intell. Syst. Technol..

[49]  Robert C. Green,et al.  A fuzzy adaptive comfort temperature model with grey predictor for multi-agent control system of smart building , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[50]  L. Berglund,et al.  A standard predictive index of human response to the thermal environment , 1986 .

[51]  M. M. Ardehali,et al.  Genetic algorithm-based fuzzy-PID control methodologies for enhancement of energy efficiency of a dynamic energy system , 2011 .

[52]  Gary R. Weckman,et al.  An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks , 2020, Energies.

[53]  Wenjian Cai,et al.  Operating points as communication bridge between energy evaluation with air temperature and velocity based on extreme learning machine (ELM) models , 2016, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA).

[54]  Gail Brager,et al.  Developing an adaptive model of thermal comfort and preference , 1998 .

[55]  Ming Wang,et al.  Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method , 2013, Soft Computing.

[56]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[57]  Yacine Rezgui,et al.  A smart heating set point scheduler using an artificial neural network and genetic algorithm , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[58]  Xiaopeng Li,et al.  Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller , 2020, Building and Environment.

[59]  Irraivan Elamvazuthi,et al.  Intelligent multi-objective control and management for smart energy efficient buildings , 2016 .

[60]  Tianshu Wei,et al.  Deep reinforcement learning for building HVAC control , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

[61]  Munther A. Dahleh,et al.  Data-driven control of micro-climate in buildings; an event-triggered reinforcement learning approach , 2020, Applied Energy.

[62]  Jin Woo Moon,et al.  Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model , 2016 .

[63]  D. Kolokotsaa,et al.  Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks , 2003 .

[64]  Burcin Becerik-Gerber,et al.  A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points , 2014 .

[65]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[66]  Manfred Morari,et al.  NeurOpt: Neural network based optimization for building energy management and climate control , 2020, L4DC.

[67]  Jie Li,et al.  Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning , 2019, ArXiv.

[68]  Yeng Chai Soh,et al.  Balancing indoor thermal comfort and energy consumption of ACMV systems via sparse swarm algorithms in optimizations , 2017 .

[69]  Jonghoon Ahn,et al.  Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system , 2017 .

[70]  Anastasios I. Dounis,et al.  Intelligent Coordinator of Fuzzy Controller-Agents for Indoor Environment Control in Buildings Using 3-D Fuzzy Comfort Set , 2007, 2007 IEEE International Fuzzy Systems Conference.

[71]  T. S. Liu,et al.  Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving , 2015, IEEE Transactions on Automation Science and Engineering.

[72]  R. Garcia-Martinez,et al.  Fuzzy Control For Improving Energy Management Within Indoor Building Environments , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[73]  Murali Annavaram,et al.  The Occupant Mobile Gateway: A participatory sensing and machine-learning approach for occupant-aware energy management , 2017 .

[74]  R. Andersen,et al.  Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions , 2009 .

[75]  Yacine Rezgui,et al.  An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings , 2017, IEEE Transactions on Automation Science and Engineering.

[76]  Hossam A. Gabbar,et al.  Comfort-based fuzzy control optimization for energy conservation in HVAC systems , 2014 .

[77]  Francisco Herrera,et al.  A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems , 2005, Eng. Appl. Artif. Intell..

[78]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[79]  C. Treeck,et al.  The changing context of comfort in an unpredictable world , 2012 .

[80]  Nuria Forcada,et al.  Implementation of predictive control in a commercial building energy management system using neural networks , 2017 .

[81]  He Lu-hua.  Discussion on Liaoning Province Design Standard for Energy Efficiency of Residential Buildings , 2008 .

[82]  Jonghoon Ahn,et al.  Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments , 2017 .

[83]  Mignon Park,et al.  Fuzzy logic-based advanced on–off control for thermal comfort in residential buildings , 2015 .

[84]  Robert Sabourin,et al.  Two-objective on-line optimization of supervisory control strategy , 2004 .

[85]  Ülle Kotta,et al.  Learning based personalized energy management systems for residential buildings , 2016 .

[86]  Siliang Lu,et al.  A DEEP REINFORCEMENT LEARNING APPROACH TO USINGWHOLE BUILDING ENERGYMODEL FOR HVAC OPTIMAL CONTROL , 2018 .

[87]  Gian Marco Revel,et al.  Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV , 2018 .

[88]  Borut Zupančič,et al.  Fuzzy control for the illumination and temperature comfort in a test chamber , 2005 .

[89]  Yaolin Lin,et al.  Adaptive optimal control model for building cooling and heating sources , 2008 .

[90]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[91]  Anastasios I. Dounis,et al.  Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques , 2011 .

[92]  Ruxu Du,et al.  Design of intelligent comfort control system with human learning and minimum power control strategies , 2008 .

[93]  Dimitrios Gyalistras,et al.  Potential Assessment of Rule-Based Control for Integrated Room Automation , 2010 .

[94]  José A. Candanedo,et al.  A practical artificial intelligence-based approach for predictive control in commercial and institutional buildings , 2020 .

[95]  Ji-Hyun Lee,et al.  Application of control logic for optimum indoor thermal environment in buildings with double skin envelope systems , 2014 .

[96]  Maizura Mokhtar,et al.  Multi-agent Gaussian Adaptive Resonance Theory Map for building energy control and thermal comfort management of UCLan's WestLakes Samuel Lindow Building , 2014 .

[97]  Jianhong Wu,et al.  Towards non-intrusive and high accuracy prediction of personal thermal comfort using a few sensitive physiological parameters , 2020 .

[98]  M. M. Gouda,et al.  Thermal comfort based fuzzy logic controller , 2001 .

[99]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[100]  Max H. Sherman,et al.  A simplified model of thermal comfort , 1985 .

[101]  Er-Wei Bai,et al.  Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification , 2016 .

[102]  Burcin Becerik-Gerber,et al.  Towards Optimization of Building Energy and Occupant Comfort Using Multi-Agent Simulation , 2011 .

[103]  Nathan Mendes,et al.  PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants , 2007, 2007 IEEE International Conference on Control Applications.

[104]  Nursyarizal Mohd Nor,et al.  Stochastic optimized intelligent controller for smart energy efficient buildings , 2014 .

[105]  António E. Ruano,et al.  The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems , 2016 .

[106]  Mine Hiroshi,et al.  Real-time optimized HVAC control system on top of an IoT framework , 2018, 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC).

[107]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[108]  Lingfeng Wang,et al.  Development of multi-agent system for building energy and comfort management based on occupant behaviors , 2013 .

[109]  Nadeem Javaid,et al.  Comfort evaluation of seasonally and daily used residential load in smart buildings for hottest areas via predictive mean vote method , 2020, Sustain. Comput. Informatics Syst..

[110]  Gianfranco Rizzo,et al.  The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller , 2004 .

[111]  Lingfeng Wang,et al.  Energy management of multi-zone buildings based on multi-agent control and particle swarm optimization , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[112]  Paul Davidsson,et al.  Distributed monitoring and control of office buildings by embedded agents , 2005, Inf. Sci..

[113]  Nicholas R. Jennings,et al.  A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households , 2013, BuildSys@SenSys.

[114]  Wil L. Kling,et al.  Smart grid and smart building inter-operation using agent-based particle swarm optimization , 2015 .

[115]  Biao Huang,et al.  A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems , 2017 .

[116]  Milos Manic,et al.  Fuzzy linguistic knowledge based behavior extraction for building energy management systems , 2013, 2013 6th International Symposium on Resilient Control Systems (ISRCS).

[117]  Semiha Ergan,et al.  Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network , 2020 .

[118]  Manfred Morari,et al.  Increasing energy efficiency in building climate control using weather forecasts and model predictive control , 2010 .

[119]  Burcin Becerik-Gerber,et al.  User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings , 2014 .

[120]  Antonio Messineo,et al.  A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input , 2014 .

[121]  Ming Rao,et al.  Knowledge-based automation for energy conservation and indoor air quality control in HVAC processes , 1993 .

[122]  Dennis L. Loveday,et al.  Identifying a suitable method for studying thermal comfort in people’s homes , 2012 .

[123]  Bing Dong,et al.  A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.

[124]  Lingfeng Wang,et al.  Multi-agent control system with information fusion based comfort model for smart buildings , 2012 .

[125]  Mincho Hadjiski,et al.  Thermal comfort-based MPC of air handling unit , 2006 .

[126]  Lian Zhao,et al.  Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems , 2009 .

[127]  D. Kolokotsa,et al.  Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction , 2001 .

[128]  Julien Eynard,et al.  Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings , 2015, Appl. Soft Comput..

[129]  Kalyan Veeramachaneni,et al.  Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems , 2014, J. Ambient Intell. Smart Environ..

[130]  Yonghong Huang,et al.  Indoor Thermal Comfort Control Research Based on Adaptive Fuzzy Strategy , 2006, The Proceedings of the Multiconference on "Computational Engineering in Systems Applications".

[131]  Mincho Hadjiski,et al.  MULTI AGENT INTELLIGENT CONTROL OF CENTRALIZED HVAC SYSTEMS , 2006 .

[132]  Kuang-Chin Lu,et al.  Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm , 2019, Building and Environment.

[133]  Jean-Louis Scartezzini,et al.  Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand , 2017, Energy and Buildings.

[134]  Nathan Mendes,et al.  TED-AJ03-258 USING FUZZY LOGIC IN HEATING CONTROL SYSTEMS , 2003 .

[135]  D. Kolokotsa,et al.  Comparison of the performance of fuzzy controllers for the management of the indoor environment , 2003 .

[136]  Denia Kolokotsa,et al.  Interconnecting smart card system with PLC controller in a local operating network to form a distributed energy management and control system for buildings , 2002 .

[137]  Zhixing Li,et al.  Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI , 2020, Future Internet.

[138]  Dimitrios Soudris,et al.  Towards plug&play smart thermostats inspired by reinforcement learning , 2018, INTESA@ESWEEK.

[139]  Sergio Gil-Lopez,et al.  An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques , 2017 .

[140]  Thananchai Leephakpreeda,et al.  Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage , 2004 .

[141]  Lihua Xie,et al.  A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings , 2019, Applied Energy.

[142]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[143]  G. J. Rios-Moreno,et al.  Modelling temperature in intelligent buildings by means of autoregressive models , 2007 .

[144]  Abbas Javed,et al.  Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology , 2017, IEEE Transactions on Industrial Informatics.

[145]  K. P. Moustris,et al.  Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data , 2020 .

[146]  Muhammad Awais,et al.  Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes , 2016 .

[147]  Matjaz Gams,et al.  Multi-Agent Architecture for Control of Heating and Cooling in a Residential Space , 2015, Comput. J..

[148]  Rafael Alcalá,et al.  Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms , 2003, Applied Intelligence.