Coordinated energy management for a cluster of buildings through deep reinforcement learning
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José R. Vázquez-Canteli | Alfonso Capozzoli | Zoltán Nagy | Marco Savino Piscitelli | Giuseppe Pinto | José Ramón Vázquez-Canteli | Z. Nagy | Alfonso Capozzoli | G. Pinto | M. Piscitelli
[1] José R. Vázquez-Canteli,et al. Reinforcement learning for demand response: A review of algorithms and modeling techniques , 2019, Applied Energy.
[2] Fu Xiao,et al. Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids , 2021 .
[3] Zhe Wang,et al. Reinforcement learning for building controls: The opportunities and challenges , 2020, Applied Energy.
[4] Gregor Verbic,et al. Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading , 2020 .
[5] Kaile Zhou,et al. A coordinated charging scheduling method for electric vehicles considering different charging demands , 2020 .
[6] Peter J. Stuckey,et al. Large Neighborhood Search for Temperature Control with Demand Response , 2020, CP.
[7] Nilay Shah,et al. Smart energy systems for sustainable smart cities: Current developments, trends and future directions , 2019, Applied Energy.
[8] Takashi Matsuyama,et al. Coordinated Energy Management for Inter-Community Imbalance Minimization , 2016 .
[9] José R. Vázquez-Canteli,et al. Multi-agent reinforcement learning for adaptive demand response in smart cities , 2019 .
[10] Pei Huang,et al. A hierarchical coordinated demand response control for buildings with improved performances at building group , 2019 .
[11] Chiara Delmastro,et al. Generalizable occupant-driven optimization model for domestic hot water production in NZEB , 2016 .
[12] Sergey Levine,et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , 2018, ICML.
[13] R. Belmans,et al. Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice , 2015, IEEE Transactions on Smart Grid.
[14] Henry Zhu,et al. Soft Actor-Critic Algorithms and Applications , 2018, ArXiv.
[15] Santiago Grijalva,et al. A Review of Reinforcement Learning for Autonomous Building Energy Management , 2019, Comput. Electr. Eng..
[16] José R. Vázquez-Canteli,et al. MARLISA: Multi-Agent Reinforcement Learning with Iterative Sequential Action Selection for Load Shaping of Grid-Interactive Connected Buildings , 2020, BuildSys@SenSys.
[17] Bart De Schutter,et al. Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning , 2017, IEEE Transactions on Smart Grid.
[18] Agis M. Papadopoulos,et al. A comparative review of heating systems in EU countries, based on efficiency and fuel cost , 2018, Renewable and Sustainable Energy Reviews.
[19] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[20] Alfonso Capozzoli,et al. Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings , 2020 .
[21] Johan Driesen,et al. Deep Reinforcement Learning based Optimal Control of Hot Water Systems , 2018, ArXiv.
[22] Zhengwei Li,et al. Performance evaluation of conventional demand response at building-group-level under different electricity pricings , 2016 .
[23] Siobhán Clarke,et al. Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments , 2017, ACM Trans. Auton. Adapt. Syst..
[24] José R. Vázquez-Canteli,et al. Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities , 2019, Sustainable Cities and Society.
[25] José R. Vázquez-Canteli,et al. CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning , 2019, BuildSys@SenSys.
[26] George A. Vouros,et al. Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids , 2018, Applied Energy.
[27] Farrokh Janabi-Sharifi,et al. Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .
[28] José R. Vázquez-Canteli,et al. Optimal decarbonization pathways for urban residential building energy services , 2018, Applied Energy.
[29] Nilay Shah,et al. Hierarchical price coordination of heat pumps in a building network controlled using model predictive control , 2019, Energy and Buildings.
[30] Marco Aiello,et al. Energy management for user’s thermal and power needs: A survey , 2019, Energy Reports.
[31] Gregor P. Henze,et al. Evaluation of Reinforcement Learning Control for Thermal Energy Storage Systems , 2003 .
[32] Alberto Bemporad,et al. Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities , 2018 .
[33] Seung Ho Hong,et al. Incentive-based demand response for smart grid with reinforcement learning and deep neural network , 2019, Applied Energy.
[34] Anna Joanna Marszal,et al. IEA EBC Annex 67 Energy Flexible Buildings , 2017 .
[35] Anna Scaglione,et al. Real-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling , 2013, IEEE Transactions on Smart Grid.
[36] José R. Vázquez-Canteli,et al. Balancing comfort and energy consumption of a heat pump using batch reinforcement learning with fitted Q-iteration , 2017 .
[37] Peter Dayan,et al. Q-learning , 1992, Machine Learning.