Building HVAC control with reinforcement learning for reduction of energy cost and demand charge
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Kirk H. Drees | Zhanhong Jiang | Michael J. Risbeck | Chenlu Zhang | Vish Ramamurti | M. J. Risbeck | K. H. Drees | Sugumar Murugesan | Jaume Amores | Young M. Lee | Zhanhong Jiang | Chenlu Zhang | Ramamurti Vish | S. Murugesan | Amores Jaume | M. LeeYoung | Sugumar Murugesan
[1] Daniel Urieli,et al. A learning agent for heat-pump thermostat control , 2013, AAMAS.
[2] Shiyu Yang,et al. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization , 2020 .
[3] Peter Stone,et al. Deep Recurrent Q-Learning for Partially Observable MDPs , 2015, AAAI Fall Symposia.
[4] Frauke Oldewurtel,et al. Building modeling as a crucial part for building predictive control , 2013 .
[5] Viktor K. Prasanna,et al. Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation , 2019, BuildSys@SenSys.
[6] Philip Bachman,et al. Deep Reinforcement Learning that Matters , 2017, AAAI.
[7] Ming Jin,et al. Advanced Building Control via Deep Reinforcement Learning , 2019, Energy Procedia.
[8] Yue Yuan,et al. Data-driven model predictive control for building climate control: Three case studies on different buildings , 2019, Building and Environment.
[9] Francesco Borrelli,et al. Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism , 2015, IEEE Transactions on Control Systems Technology.
[10] Semiha Ergan,et al. Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network , 2020 .
[11] Jin Wen,et al. Review of building energy modeling for control and operation , 2014 .
[12] Martin Kozek,et al. Ten questions concerning model predictive control for energy efficient buildings , 2016 .
[13] Johan Driesen,et al. Deep Reinforcement Learning for Optimal Control of Space Heating , 2018, ArXiv.
[14] Etienne Perot,et al. Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.
[15] Zicheng Cai,et al. Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy , 2019, BuildSys@SenSys.
[16] Zhe Wang,et al. Reinforcement learning for building controls: The opportunities and challenges , 2020, Applied Energy.
[17] K. Max Zhang,et al. Model predictive control of thermal storage for demand response , 2015, 2015 American Control Conference (ACC).
[18] James B. Rawlings,et al. Economic Model Predictive Control for Time-Varying Cost and Peak Demand Charge Optimization , 2020, IEEE Transactions on Automatic Control.
[19] Anas Alanqar,et al. Practice-Oriented System Identification Strategies for MPC of Building Thermal and HVAC Dynamics , 2018 .
[20] Simeng Liu,et al. Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. Theoretical foundation , 2006 .
[21] Maher Kayal,et al. Comparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs , 2019, 2019 IEEE Milan PowerTech.
[22] Gregor P. Henze,et al. Evaluation of optimal control for active and passive building thermal storage , 2004 .
[23] Sherif Abdelwahed,et al. Learning-based Model Predictive Control for Smart Building Thermal Management , 2019, 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT).
[24] Peng Xu,et al. Demand reduction in building energy systems based on economic model predictive control , 2012 .
[25] Mark Cannon,et al. Efficient nonlinear model predictive control algorithms , 2004, Annu. Rev. Control..
[26] Khee Poh Lam,et al. Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning , 2019, Energy and Buildings.
[27] Simeng Liu,et al. Evaluation of Reinforcement Learning for Optimal Control of Building Active and Passive Thermal Storage Inventory , 2005 .
[28] Costas J. Spanos,et al. HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach , 2019, IEEE Transactions on Automation Science and Engineering.
[29] Manfred Morari,et al. Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .
[30] Manfred Morari,et al. NeurOpt: Neural network based optimization for building energy management and climate control , 2020, L4DC.
[31] Simeng Liu,et al. Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 2: Results and analysis , 2006 .
[32] Yonggang Wen,et al. Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning , 2017, IEEE Transactions on Cybernetics.
[33] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[34] Tianshu Wei,et al. Deep reinforcement learning for building HVAC control , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).
[35] Sergey Levine,et al. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[36] Hao Huang,et al. Model predictive control for energy-efficient buildings: An airport terminal building study , 2014, 11th IEEE International Conference on Control & Automation (ICCA).
[37] Dario Ambrosini,et al. Data-driven model predictive control using random forests for building energy optimization and climate control , 2018, Applied Energy.
[38] Alberto Cerpa,et al. Occupancy based demand response HVAC control strategy , 2010, BuildSys '10.
[39] Alberto Bemporad,et al. From linear to nonlinear MPC: bridging the gap via the real-time iteration , 2020, Int. J. Control.
[40] Philip Haves,et al. Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.
[41] Stephen P. Boyd,et al. Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.