AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review Article

Challenges and opportunities of machine learning control in building operations

Liang Zhang1( ), Zhelun Chen2, Xiangyu Zhang3, Amanda Pertzborn4, Xin Jin3
The University of Arizona, 1209 E 2nd St, Tucson, AZ, USA
Drexel University, 3141 Chestnut St, Philadelphia, PA, USA
National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO, USA
National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, USA
Show Author Information

Abstract

Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. This paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

Keywords

machine learningreinforcement learningbuilding operation controlbuilding energy system

References

 

Afroz Z, Shafiullah G, Urmee T, et al. (2018). Modeling techniques used in building HVAC control systems: A review. Renewable and Sustainable Energy Reviews, 83: 64–84.
Crossref Google Scholar
 

Ahmad MW, Mourshed M, Yuce B, et al. (2016). Computational intelligence techniques for HVAC systems: A review.Building Simulation, 9: 359–398.
Crossref Google Scholar
 
Ahmad MW, Hippolyte J-L, Mourshed M, et al. (2017a). Random forests and artificial neural network for predicting daylight illuminance and energy consumption. In: Proceedings of the 15th International IPBSA Building Simulation Conference, San Francisco, USA.
 

Ahmad MW, Mourshed M, Rezgui Y (2017b). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147: 77–89.
Crossref Google Scholar
 
Ai T, Yu J-q, Liu Y-f, et al. (2010). Study on neural network selftuning PID control for temperature of active solar house heating system. In: Proceedings of 2010 2nd International Workshop on Intelligent Systems and Applications, Wuhan, China.
Crossref
 

Alawadi S, Mera D, Fernández-Delgado M, et al. (2022). A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings. Energy Systems, 13: 689–705.
Crossref Google Scholar
 

Ali MM, Khan MA, Shafi M, et al. (2014). Heat ventilation & air-conditioning system with self-tuning fuzzy PI controller. International Journal of Modern Engineering Research (IJMER), 4(9): 24–35.
Google Scholar
 

Ardabili SF, Mahmoudi A, Gundoshmian TM (2016). Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers. Journal of Building Engineering, 6: 301–308.
Crossref Google Scholar
 
Ardabili S, Beszedes B, Nadai L, et al. (2020). Comparative analysis of single and hybrid neuro-fuzzy-based models for an industrial heating ventilation and air conditioning control system. In: Proceedings of International Conference on Computing and Communication Technologies (2010 RIVF), Ho Chi Minh City, Vietnam.
Crossref
 
Atabay D, Herzog S, Sanger F, et al. (2013). Self-adapting building models and optimized hvac scheduling for demand side management. In: Proceedings of the 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013).
Crossref
 

Attaran SM, Yusof R, Selamat H (2016). A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system. Applied Thermal Engineering, 99: 613–624.
Crossref Google Scholar
 

Ayoub M (2020). A review on machine learning algorithms to predict daylighting inside buildings. Solar Energy, 202: 249–275.
Crossref Google Scholar
 

Bae Y, Bhattacharya S, Cui B, et al. (2021). Sensor impacts on building and HVAC controls: A critical review for building energy performance. Advances in Applied Energy, 4: 100068.
Crossref Google Scholar
 
Barrett E, Linder S (2015). Autonomous HVAC control: A reinforcement learning approach. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases.
Crossref
 

Beccali M, Bonomolo M, Ciulla G, et al. (2018). Assessment of indoor illuminance and study on best photosensors’ position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks. Energy, 154: 466–476.
Crossref Google Scholar
 
Blum D, Jorissen F, Huang S, et al. (2019). Prototyping the BOPTEST framework for simulation-based testing of advanced control strategies in buildings. Lawrence Berkeley National Laboratory.
 
Chaudhuri T, Soh YC, Li H, et al. (2017). Machine learning based prediction of thermal comfort in buildings of equatorial Singapore. In: Proceedings of 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC), Singapore.
Crossref
 
Chen Z, Zhang Y, Zhang T (2015). An intelligent control approach to home energy management under forecast uncertainties. In: Proceedings of 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Riga, Latvia.
Crossref
 

Chen Y, Norford LK, Samuelson HW, et al. (2018). Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. Energy and Buildings, 169: 195–205.
Crossref Google Scholar
 

Chen Z, Jiang C (2018). Building occupancy modeling using generative adversarial network. Energy and Buildings, 174: 372–379.
Crossref Google Scholar
 
Chen B, Cai Z, Bergés M (2019). Gnu-RL: A precocial reinforcement learning solution for building HVAC control using a differentiable MPC policy. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, USA.
Crossref
 

Cheng M-Y, Cao M-T (2014). Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Applied Soft Computing, 22: 178–188.
Crossref Google Scholar
 

Chow TT, Zhang GQ, Lin Z, et al. (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34: 103–109.
Crossref Google Scholar
 

Chua KH, Lim YS, Morris S (2017). A novel fuzzy control algorithm for reducing the peak demands using energy storage system. Energy, 122: 265–273.
Crossref Google Scholar
 

Cifuentes J, Marulanda G, Bello A, et al. (2020). Air temperature forecasting using machine learning techniques: a review. Energies, 13: 4215.
Crossref Google Scholar
 

Cole WJ, Powell KM, Hale ET, et al. (2014). Reduced-order residential home modeling for model predictive control. Energy and Buildings, 74: 69–77.
Crossref Google Scholar
 

Collotta M, Messineo A, Nicolosi G, et al. (2014). A dynamic fuzzy controller to meet thermal comfort by using neural network forecasted parameters as the input. Energies, 7: 4727–4756.
Crossref Google Scholar
 

Costa A, Keane MM, Torrens JI, et al. (2013). Building operation and energy performance: Monitoring, analysis and optimisation toolkit. Applied Energy, 101: 310–316.
Crossref Google Scholar
 

Costanzo GT, Iacovella S, Ruelens F, et al. (2016). Experimental analysis of data-driven control for a building heating system. Sustainable Energy, Grids and Networks, 6: 81–90.
Crossref Google Scholar
 

Cox SJ, Kim D, Cho H, et al. (2019). Real time optimal control of district cooling system with thermal energy storage using neural networks. Applied Energy, 238: 466–480.
Crossref Google Scholar
 

Cui C, Wu T, Hu M, et al. (2016). Short-term building energy model recommendation system: A meta-learning approach. Applied Energy, 172: 251–263.
Crossref Google Scholar
 

Dai X, Liu J, Zhang X (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223: 110159.
Crossref Google Scholar
 
Dehghani A, Khodadadi H (2017). Designing a neuro-fuzzy PID controller based on Smith predictor for heating system. In: Proceedings of 2017 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, R.O. Korea.
Crossref
 
Ding X, Du W, Cerpa A (2019). OCTOPUS: Deep reinforcement learning for holistic smart building control. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, USA.
Crossref
 

Dong B, Lam KP (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7: 89–106.
Crossref Google Scholar
 
Doshi-Velez F, Kim B (2017). Towards a rigorous science of interpretable machine learning. arXiv: 1702.08608.
 
Du D, Li X (2010). A self-learning fuzzy control method based on RBF neural networks. In: Proceedings of 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China.
Crossref
 

Esen H, Inalli M, Sengur A, et al. (2008). Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy and Buildings, 40: 1074–1083.
Crossref Google Scholar
 

Fan C, Wang J, Gang W, et al. (2019a). Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Applied Energy, 236: 700–710.
Crossref Google Scholar
 

Fan C, Xiao F, Yan C, et al. (2019b). A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning. Applied Energy, 235: 1551–1560.
Crossref Google Scholar
 

Fan C, Xiao F, Zhao Y (2017). A short-term building cooling load prediction method using deep learning algorithms. Applied Energy, 195: 222–233.
Crossref Google Scholar
 

Fazenda P, Veeramachaneni K, Lima P, et al. (2014). Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems. Journal of Ambient Intelligence and Smart Environments, 6: 675–690.
Crossref Google Scholar
 
Fiducioso M, Curi S, Schumacher B, et al. (2019). Safe contextual Bayesian optimization for sustainable room temperature PID control tuning. arXiv: 1906.12086.
Crossref
 

Finck C, Li R, Zeiler W (2019). Economic model predictive control for demand flexibility of a residential building. Energy, 176: 365–379.
Crossref Google Scholar
 

Florita AR, Henze GP (2009). Comparison of short-term weather forecasting models for model predictive control. HVAC&R Research, 15: 835–853.
Crossref Google Scholar
 
Fujimoto S, Meger D, Precup D (2019). Off-policy deep reinforcement learning without exploration. In: Proceedings of the 36th International Conference on Machine Learning.
 
Gao G, Li J, Wen Y (2019). Energy-efficient thermal comfort control in smart buildings via deep reinforcement learning. arXiv: 1901.04693.
 

Garnier A, Eynard J, Caussanel M, et al. (2014). Low computational cost technique for predictive management of thermal comfort in non-residential buildings. Journal of Process Control, 24: 750–762.
Crossref Google Scholar
 

Ghadi YY, Rasul MMG, Khan MKK (2014). Recent developments of advanced fuzzy logic controllers used in smart buildings in subtropical climate. Energy Procedia, 61: 1021–1024.
Crossref Google Scholar
 

Ghadi YY, Rasul MG, Khan MMK (2016). Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland. Renewable and Sustainable Energy Reviews, 54: 738–744.
Crossref Google Scholar
 

Guo Y, Wang J, Chen H, et al. (2018). Machine learning-based thermal response time ahead energy demand prediction for building heating systems. Applied Energy, 221: 16–27.
Crossref Google Scholar
 
Hagerman J (2014). Buildings-to-grid technical opportunities: introduction and vision. EERE Publication and Product Library.
Crossref
 

Han M, May R, Zhang X, et al. (2019). A review of reinforcement learning methodologies for controlling occupant comfort in buildings. Sustainable Cities and Society, 51: 101748.
Crossref Google Scholar
 

Han M, May R, Zhang X, et al. (2020). A novel reinforcement learning method for improving occupant comfort via window opening and closing. Sustainable Cities and Society, 61: 102247.
Crossref Google Scholar
 
Hidalgo-León R, Litardo J, Urquizo J, et al. (2019). Some factors involved in the improvement of building energy consumption: A brief review. In: Proceedings of 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador.
Crossref
 

Hong T, Wang Z, Luo X, et al. (2020). State-of-the-art on research and applications of machine learning in the building life cycle. Energy and Buildings, 212: 109831.
Crossref Google Scholar
 

Hosoz M, Ertunç HM, Bulgurcu H (2007). Performance prediction of a cooling tower using artificial neural network. Energy Conversion and Management, 48: 1349–1359.
Crossref Google Scholar
 

Hu J, Olbina S (2011). Illuminance-based slat angle selection model for automated control of split blinds. Building and Environment, 46: 786–796.
Crossref Google Scholar
 

Huang H, Chen L, Hu E (2015a). A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy and Buildings, 97: 86–97.
Crossref Google Scholar
 

Huang H, Chen L, Hu E (2015b). A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Building and Environment, 89: 203–216.
Crossref Google Scholar
 

Hussain S, Gabbar HA, Bondarenko D, et al. (2014). Comfort-based fuzzy control optimization for energy conservation in HVAC systems. Control Engineering Practice, 32: 172–182.
Crossref Google Scholar
 

Idowu S, Saguna S, Åhlund C, et al. (2016). Applied machine learning: forecasting heat load in district heating system. Energy and Buildings, 133: 478–488.
Crossref Google Scholar
 
Jain A, Smarra F, Mangharam R (2017). Data predictive control using regression trees and ensemble learning. In: Proceedings of 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia.
Crossref
 
Javed A, Larijani H, Ahmadinia A, et al. (2014). Comparison of the robustness of RNN, MPC and ANN controller for residential heating system. In: Proceedings of 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, Sydney, Australia.
Crossref
 

Jin Y, Yan D, Kang X, et al. (2021). Forecasting building occupancy: A temporal-sequential analysis and machine learning integrated approach. Energy and Buildings, 252: 111362.
Crossref Google Scholar
 

Jordan MI, Mitchell TM (2015). Machine learning: Trends, perspectives, and prospects. Science, 349: 255–260.
Crossref Google Scholar
 

Kamel E, Sheikh S, Huang X (2020). Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days. Energy, 206: 118045.
Crossref Google Scholar
 

Karunamurthy K, Chatterjee D, Pattanashetti S, et al. (2020). Prediction of thermal performance of cooling tower of a chiller plant using machine learning. IOP Conference Series: Earth and Environmental Science, 573: 012029.
Crossref Google Scholar
 

Kaur A, Kaur A (2012). Development of neuro fuzzy controller algorithm for air conditioning system. International Journal of Engineering Science and Technology (IJEST), 4: 1667–1671.
Google Scholar
 

Kazmi H, Mehmood F, Lodeweyckx S, et al. (2018). Gigawatt-hour scale savings on a budget of zero: Deep reinforcement learning based optimal control of hot water systems. Energy, 144: 159–168.
Crossref Google Scholar
 
Kim Y, Park C (2014). Nonlinear predictive control of chiller system using gaussian process model. In: Proceedings of the 2nd Asia Conference of International Building Performance Simulation Association, Nagoya, Japan.
 

Kim W, Jeon Y, Kim Y (2016). Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method. Applied Energy, 162: 666–674.
Crossref Google Scholar
 

Kim J-H, Seong N-C, Choi W (2019). Modeling and optimizing a chiller system using a machine learning algorithm. Energies, 12: 2860.
Crossref Google Scholar
 

Kumar R, Aggarwal RK, Sharma JD (2013). Energy analysis of a building using artificial neural network: A review. Energy and Buildings, 65: 352–358.
Crossref Google Scholar
 
Kumar R, Khatri SK, Diván MJ (2021). Data center air handling unit fan speed optimization using machine learning techniques. In: Proceedings of 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.
Crossref
 

Kwok SSK, Lee EWM (2011). A study of the importance of occupancy to building cooling load in prediction by intelligent approach. Energy Conversion and Management, 52: 2555–2564.
Crossref Google Scholar
 

Lazos D, Sproul AB, Kay M (2014). Optimisation of energy management in commercial buildings with weather forecasting inputs: A review. Renewable and Sustainable Energy Reviews, 39: 587–603.
Crossref Google Scholar
 

Lee C, Chen R (2015). Optimal self-tuning PID controller based on low power consumption for a server fan cooling system. Sensors, 15: 11685–11700.
Crossref Google Scholar
 

Lee YM, Horesh R, Liberti L (2015). Optimal HVAC control as demand response with on-site energy storage and generation system. Energy Procedia, 78: 2106–2111.
Crossref Google Scholar
 
Li S, Ren S, Wang X (2013). HVAC room temperature prediction control based on neural network model. In: Proceedings of 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation, Hong Kong, China.
 
Li B, Xia L (2015). A multi-grid reinforcement learning method for energy conservation and comfort of HVAC in buildings. In: Proceedings of 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden.
Crossref
 

Li Z, Dong B (2018). Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches. Energy and Buildings, 158: 268–281.
Crossref Google Scholar
 

Li G, Yao Q, Fan C, et al. (2021). An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems. Building and Environment, 203: 108057.
Crossref Google Scholar
 

Liang W, Quinte R, Jia X, et al. (2015). MPC control for improving energy efficiency of a building air handler for multi-zone VAVs. Building and Environment, 92: 256–268.
Crossref Google Scholar
 

Lissa P, Schukat M, Barrett E (2020). Transfer learning applied to reinforcement learning-based HVAC control. SN Computer Science, 1: 127.
Crossref Google Scholar
 

Liu S, Henze GP (2006). Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory. Part 2: Results and analysis. Energy and Buildings, 38: 148–161.
Crossref Google Scholar
 

Liu H, Lee S, Kim M, et al. (2013). Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system. Energy and Buildings, 66: 553–561.
Crossref Google Scholar
 

Lusis P, Khalilpour KR, Andrew L, et al. (2017). Short-term residential load forecasting: Impact of calendar effects and forecast granularity. Applied Energy, 205: 654–669.
Crossref Google Scholar
 

Ma J, Qin J, Salsbury T, et al. (2012). Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 67: 92–100.
Crossref Google Scholar
 

Ma N, Aviv D, Guo H, et al. (2021). Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality. Renewable and Sustainable Energy Reviews, 135: 110436.
Crossref Google Scholar
 
Madhikermi M, Malhi AK, Främling K (2019). Explainable artificial intelligence based heat recycler fault detection in air handling unit. In: Proceedings of International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems.
Crossref
 

Manfren M, James PA, Tronchin L (2022). Data-driven building energy modelling–An analysis of the potential for generalisation through interpretable machine learning. Renewable and Sustainable Energy Reviews, 167: 112686.
Crossref Google Scholar
 

Manjarres D, Mera A, Perea E, et al. (2017). An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques. Energy and Buildings, 152: 409–417.
Crossref Google Scholar
 

Mason K, Grijalva S (2019). A review of reinforcement learning for autonomous building energy management. Computers & Electrical Engineering, 78: 300–312.
Crossref Google Scholar
 

Mayne DQ (2014). Model predictive control: Recent developments and future promise. Automatica, 50: 2967–2986.
Crossref Google Scholar
 

Mba L, Meukam P, Kemajou A (2016). Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy and Buildings, 121: 32–42.
Crossref Google Scholar
 
Merabti S, Draoui B, Bounaama F (2016). A review of control systems for energy and comfort management in buildings. In: Proceedings of 2016 8th International Conference on Modelling, Identification and Control (ICMIC).
Crossref
 
Mirinejad H, Welch KC, Spicer L (2012). A review of intelligent control techniques in HVAC systems. In: Proceedings of 2012 IEEE Energytech, Cleveland, OH, USA.
Crossref
 
Mnih V, Badia AP, Mirza M, et al. (2016). Asynchronous methods for deep reinforcement learning. In: Proceedings of International Conference on Machine Learning.
 

Monfet D, Corsi M, Choinière D, et al. (2014). Development of an energy prediction tool for commercial buildings using case-based reasoning. Energy and Buildings, 81: 152–160.
Crossref Google Scholar
 
Mosaico G, Saviozzi M, Silvestro F, et al. (2019). Simplified state space building energy model and transfer learning based occupancy estimation for HVAC optimal control. In: Proceeding of 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), Florence, Italy.
Crossref
 

Mosavi A, Salimi M, Faizollahzadeh Ardabili S, et al. (2019). State of the art of machine learning models in energy systems, a systematic review. Energies, 12: 1301.
Crossref Google Scholar
 

Mustafaraj G, Lowry G, Chen J (2011). Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office. Energy and Buildings, 43: 1452–1460.
Crossref Google Scholar
 
Nagy A, Kazmi H, Cheaib F, et al. (2018). Deep reinforcement learning for optimal control of space heating. arXiv: 1805.03777.
 

Naidu DS, Rieger CG (2011). Advanced control strategies for HVAC&R systems—An overview: part II: soft and fusion control. HVAC&R Research, 17: 144–158.
Crossref Google Scholar
 

Najafi M, Auslander DM, Bartlett PL, et al. (2012). Application of machine learning in the fault diagnostics of air handling units. Applied Energy, 96: 347–358.
Crossref Google Scholar
 

O’Neill Z, O’Neill C (2016). Development of a probabilistic graphical model for predicting building energy performance. Applied Energy, 164: 650–658.
Crossref Google Scholar
 
Oldewurtel F, Ulbig A, Parisio A, et al. (2010). Reducing peak electricity demand in building climate control using real-time pricing and model predictive control. In: Proceedings of 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA.
Crossref
 

Oldewurtel F, Parisio A, Jones CN, et al. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45: 15–27.
Crossref Google Scholar
 
Overgaard A, Nielsen BK, Kallesøe CS, et al. (2019). Reinforcement learning for mixing loop control with flow variable eligibility trace. In: Proceedings of 2019 IEEE Conference on Control Technology and Applications (CCTA), Hong Kong, China.
Crossref
 

Papantoniou S, Kolokotsa D, Kalaitzakis K (2015). Building optimization and control algorithms implemented in existing BEMS using a web based energy management and control system. Energy and Buildings, 98: 45–55.
Crossref Google Scholar
 

Parisio A, Rikos E, Glielmo L (2014). A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 22: 1813–1827.
Crossref Google Scholar
 

Park JY, Dougherty T, Fritz H, et al. (2019a). LightLearn: an adaptive and occupant centered controller for lighting based on reinforcement learning. Building and Environment, 147: 397–414.
Crossref Google Scholar
 

Park S, Ahn KU, Hwang A, et al. (2019b). Artificial Neural Network Models for Optimal Start and Stop of Chiller and AHU. Journal of the Architectural Institute of Korea Structure and Construction, 35: 45–52.
Google Scholar
 

Park S, Ahn KU, Hwang S, et al. (2019c). Machine learning vs. hybrid machine learning model for optimal operation of a chiller. Science and Technology for the Built Environment, 25: 209–220.
Crossref Google Scholar
 
Paternain S, Calvo-Fullana M, Chamon LFO, et al. (2019). Safe policies for reinforcement learning via primal-dual methods. arXiv: 1911.09101.
 
Peng KS, Morrison CT (2016). Model Predictive Prior Reinforcement Learning for a Heat Pump Thermostat. In: Proceedings of IEEE International Conference on Automatic Computing: Feedback Computing.
 

Pinto G, Messina R, Li H, et al. (2022a). Sharing is caring: an extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics. Energy and Buildings, 276: 112530.
Crossref Google Scholar
 

Pinto G, Wang Z, Roy A, et al. (2022b). Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives. Advances in Applied Energy, 5: 100084.
Crossref Google Scholar
 

Potočnik P, Vidrih B, Kitanovski A, et al. (2019). Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings.Building Simulation, 12: 1077–1093.
Crossref Google Scholar
 
Raman NS, Devraj AM, Barooah P, et al. (2020). Reinforcement learning for control of building HVAC systems. 2020 American Control Conference (ACC), Denver, CO, USA.
Crossref
 

Ruano A, Pesteh S, Silva S, et al. (2016). PVM-based intelligent predictive control of HVAC systems. IFAC-PapersOnLine, 49: 371–376.
Crossref Google Scholar
 

Ruelens F, Claessens BJ, Vandael S, et al. (2017). Residential demand response of thermostatically controlled loads using batch reinforcement learning. IEEE Transactions on Smart Grid, 8: 2149–2159.
Crossref Google Scholar
 

Ryu SH, Moon HJ (2016). Development of an occupancy prediction model using indoor environmental data based on machine learning techniques. Building and Environment, 107: 1–9.
Crossref Google Scholar
 

Sala-Cardoso E, Delgado-Prieto M, Kampouropoulos K, et al. (2018). Activity-aware HVAC power demand forecasting. Energy and Buildings, 170: 15–24.
Crossref Google Scholar
 

Salimi S, Hammad A (2019). Critical review and research roadmap of office building energy management based on occupancy monitoring. Energy and Buildings, 182: 214–241.
Crossref Google Scholar
 
Schulman J, Wolski F, Dhariwal P, et al. (2017). Proximal policy optimization algorithms. arXiv: 1707.06347.
 

Seyedzadeh S, Rahimian FP, Glesk I, et al. (2018). Machine learning for estimation of building energy consumption and performance: a review. Visualization in Engineering, 6: 5.
Crossref Google Scholar
 
Sharifian MBB, Mirlo A, Tavoosi J, Sabahi M (2011). Self-adaptive RBF neural network PID controller in linear elevator. In: Proceedings of 2011 International Conference on Electrical Machines and Systems, Beijing, China.
Crossref
 

Smarra F, Jain A, de Rubeis T, et al. (2018). Data-driven model predictive control using random forests for building energy optimization and climate control. Applied Energy, 226: 1252–1272.
Crossref Google Scholar
 
Song Y (2014). Intelligent PID controller bassed on fuzzy logic control and neural network technology for indoor environment quality improvement. PhD Thesis, University of Nottingham, UK.
 

Swider DJ, Browne MW, Bansal PK, Kecman V (2001). Modelling of vapour-compression liquid chillers with neural networks. Applied Thermal Engineering, 21: 311–329.
Crossref Google Scholar
 

Takatsu H, Kawano T, Kitano K (1991). Intelligent self-tuning PID controller. IFAC Proceedings Volumes, 24: 11–15.
Crossref Google Scholar
 

Tang F, Kusiak A, Wei X (2014). Modeling and short-term prediction of HVAC system with a clustering algorithm. Energy and Buildings, 82: 310–321.
Crossref Google Scholar
 

Touzani S, Prakash AK, Wang Z, et al. (2021). Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency. Applied Energy, 304: 117733.
Crossref Google Scholar
 

Ursu I, Nastase I, Caluianu S, et al. (2013). Intelligent control of HVAC systems. Part I: Modeling and synthesis. Incas Bulletin, 5: 103–118.
Crossref Google Scholar
 

Vázquez-Canteli JR, Nagy Z (2019). Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy, 235: 1072–1089.
Crossref Google Scholar
 

Verrilli F, Srinivasan S, Gambino G, et al. (2017). Model predictive control-based optimal operations of district heating system with thermal energy storage and flexible loads. IEEE Transactions on Automation Science and Engineering, 14: 547–557.
Crossref Google Scholar
 

Wagiman KR, Abdullah MN, Hassan MY, et al. (2020). Lighting system control techniques in commercial buildings: Current trends and future directions. Journal of Building Engineering, 31: 101342.
Crossref Google Scholar
 

Wahid F, Kim D (2016). A prediction approach for demand analysis of energy consumption using K-nearest neighbor in residential buildings. International Journal of Smart Home, 10: 97–108.
Crossref Google Scholar
 

Wang L (2013). The application of fuzzy neural network to boiler steam pressure control. International Journal of Computer Science Issues (IJCSI), 10: 704–707.
Google Scholar
 

Wang Z, Hong T, Piette MA (2019). Data fusion in predicting internal heat gains for office buildings through a deep learning approach. Applied Energy, 240: 386–398.
Crossref Google Scholar
 

Wang Z, Hong T (2020). Reinforcement learning for building controls: The opportunities and challenges. Applied Energy, 269: 115036.
Crossref Google Scholar
 

Wei X, Kusiak A, Li M, et al. (2015). Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy, 83: 294–306.
Crossref Google Scholar
 
Wei T, Wang Y, Zhu Q (2017). Deep reinforcement learning for building HVAC control. In: Proceedings of the 54th Annual Design Automation Conference 2017, Austin, TX, USA.
Crossref
 

Wei W, Ramalho O, Malingre L, et al. (2019). Machine learning and statistical models for predicting indoor air quality. Indoor Air, 29: 704–726.
Crossref Google Scholar
 

Wu Z, Li N, Peng J, et al. (2018). Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings. Energy and Buildings, 173: 117–127.
Crossref Google Scholar
 

Xu C, Chen H, Wang J, et al. (2019). Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method. Building and Environment, 148: 128–135.
Crossref Google Scholar
 

Yang Z, Li N, Becerik-Gerber B, et al. (2014). A systematic approach to occupancy modeling in ambient sensor-rich buildings. SIMULATION, 90: 960–977.
Crossref Google Scholar
 

Yang L, Nagy Z, Goffin P, et al. (2015). Reinforcement learning for optimal control of low exergy buildings. Applied Energy, 156: 577–586.
Crossref Google Scholar
 

Yang S, Wan MP, Chen W, et al. (2020). Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy, 271: 115147.
Crossref Google Scholar
 

Yang S, Wan MP, Chen W, et al. (2021). Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. Applied Energy, 288: 116648.
Crossref Google Scholar
 

Yildiz B, Bilbao JI, Sproul AB (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73: 1104–1122.
Crossref Google Scholar
 

Yousaf A, Asif RM, Shakir M, et al. (2021). A novel machine learning-based price forecasting for energy management systems. Sustainability, 13: 12693.
Crossref Google Scholar
 

Yu Z, Dexter A (2010). Online tuning of a supervisory fuzzy controller for low-energy building system using reinforcement learning. Control Engineering Practice, 18: 532–539.
Crossref Google Scholar
 

Yu Z, Haghighat F, Fung BCM, et al. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42: 1637–1646.
Crossref Google Scholar
 
Zhang Y, Zhao D, Zhang J (2011a). Research on PID controller based on the BP neural network. In: Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, Harbin, China.
 

Zhang Y, Zuo W-h, Lin T (2011b). Application of BP neural network PID control in air-conditioning system. Computer Simulation, 28(4): 149–151. (in Chinese)
Google Scholar
 
Zhang L (2018). Data-driven whole building energy forecasting model for data predictive control. PhD Thesis, Drexel University, USA.
 
Zhang Z, Lam KP (2018). Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system. In: Proceedings of the 5th Conference on Systems for Built Environments, Shenzen, China.
Crossref
 

Zhang L, Wen J (2019). A systematic feature selection procedure for short-term data-driven building energy forecasting model development. Energy and Buildings, 183: 428–442.
Crossref Google Scholar
 
Zhang C, Kuppannagari SR, Kannan R, et al. (2019a). Building HVAC scheduling using reinforcement learning via neural network based model approximation. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, USA.
Crossref
 

Zhang Z, Chong A, Pan Y, et al. (2019b). Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning. Energy and Buildings, 199: 472–490.
Crossref Google Scholar
 

Zhang L, Frank S, Kim J, et al. (2020a). A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings. Building and Environment, 186: 107338.
Crossref Google Scholar
 
Zhang X, Chintala R, Bernstein A, et al. (2020b). Grid-interactive multi-zone building control using reinforcement learning with global-local policy search. arXiv: 2010.06718.
Crossref
 
Zhang X, Jin X, Tripp C, et al. (2020c). Transferable reinforcement learning for smart homes. In: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities.
Crossref
 

Zhang L (2021). Data-driven building energy modeling with feature selection and active learning for data predictive control. Energy and Buildings, 252: 111436.
Crossref Google Scholar
 

Zhang L, Wen J (2021). Active learning strategy for high fidelity short-term data-driven building energy forecasting. Energy and Buildings, 244: 111026.
Crossref Google Scholar
 

Zhang L, Leach M, Bae Y, et al. (2021a). Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review. Advances in Applied Energy, 3: 100055.
Crossref Google Scholar
 

Zhang L, Wen J, Li Y, et al. (2021b). A review of machine learning in building load prediction. Applied Energy, 285: 116452.
Crossref Google Scholar
 

Zhang W, Wen Y, Tseng KJ, et al. (2021c). Demystifying thermal comfort in smart buildings: An interpretable machine learning approach. IEEE Internet of Things Journal, 8: 8021–8031.
Crossref Google Scholar
 

Zhang X, Biagioni D, Cai M, et al. (2021d). An edge-cloud integrated solution for buildings demand response using reinforcement learning. IEEE Transactions on Smart Grid, 12: 420–431.
Crossref Google Scholar
 
Zhou Y, Li D, Spanos CJ (2015). Learning optimization friendly comfort model for HVAC model predictive control. In: Proceedings of 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA.
Crossref
Building Simulation
Volume 16 Issue 6,
June 2023
Pages 831-852
DOI: 10.1007/s12273-023-0984-6
Cite this article:
Zhang L, Chen Z, Zhang X, et al. Challenges and opportunities of machine learning control in building operations. Building Simulation, 2023, 16(6): 831-852. https://doi.org/10.1007/s12273-023-0984-6

454

Views

8

Crossref

9

Web of Science

11

Scopus

0

CSCD

Altmetrics
Received: 23 September 2022
Revised: 09 December 2022
Accepted: 26 December 2022
Published: 14 March 2023
This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023
Return