Approximate model predictive building control via machine learning
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DOI: 10.1016/j.apenergy.2018.02.156
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- Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
- Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
- Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
- Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
- Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
- Savadkoohi, Marjan & Macarulla, Marcel & Casals, Miquel, 2023. "Facilitating the implementation of neural network-based predictive control to optimize building heating operation," Energy, Elsevier, vol. 263(PB).
- Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
- Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Hu, R.L. & Granderson, J. & Auslander, D.M. & Agogino, A., 2019. "Design of machine learning models with domain experts for automated sensor selection for energy fault detection," Applied Energy, Elsevier, vol. 235(C), pages 117-128.
- Byung-Ki Jeon & Eui-Jong Kim & Younggy Shin & Kyoung-Ho Lee, 2018. "Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems," Sustainability, MDPI, vol. 11(1), pages 1-16, December.
- Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
- Alsalemi, Abdullah & Ramadan, Mona & Bensaali, Faycal & Amira, Abbes & Sardianos, Christos & Varlamis, Iraklis & Dimitrakopoulos, George, 2019. "Endorsing domestic energy saving behavior using micro-moment classification," Applied Energy, Elsevier, vol. 250(C), pages 1302-1311.
- Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
- Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
- Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
- Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
- Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
- Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
- Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
- Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
- Kyoumars Habibi & Seyedeh Maryam Hoseini & Majid Dehshti & Mojtaba Khanian & Amir Mosavi, 2020. "The Impact of Natural Elements on Environmental Comfort in the Iranian-Islamic Historical City of Isfahan," IJERPH, MDPI, vol. 17(16), pages 1-18, August.
- Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
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Keywords
Building climate control; Model predictive control; Machine learning; Dimensionality reduction; Time delay neural networks; Regression trees;All these keywords.
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