Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads
暂无分享,去创建一个
Johan A. K. Suykens | Johan Driesen | Hussain Kazmi | Attila Balint | J. Suykens | J. Driesen | H. Kazmi | Attila Bálint
[1] Jr-Shin Li,et al. A phase model approach for thermostatically controlled load demand response , 2018, Applied Energy.
[2] R. Belmans,et al. Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice , 2015, IEEE Transactions on Smart Grid.
[3] Dane Christensen,et al. Foresee: A user-centric home energy management system for energy efficiency and demand response , 2017 .
[4] Hussain Kazmi,et al. Minimizing Grid Interaction of Solar Generation and DHW Loads in nZEBs Using Model-Free Reinforcement Learning , 2017, DARE@PKDD/ECML.
[5] Bart De Schutter,et al. Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning , 2017, IEEE Transactions on Smart Grid.
[6] Michael Chertkov,et al. Ensemble Control of Cycling Energy Loads: Markov Decision Approach , 2017, ArXiv.
[7] Michael Stadler,et al. Quantifying Flexibility of Commercial and Residential Loads for Demand Response using Setpoint Changes , 2016 .
[8] Chiara Delmastro,et al. Generalizable occupant-driven optimization model for domestic hot water production in NZEB , 2016 .
[9] Vitaly Shmatikov,et al. Privacy-preserving deep learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[10] Zheng Wen,et al. Optimal Demand Response Using Device-Based Reinforcement Learning , 2014, IEEE Transactions on Smart Grid.
[11] Tyrone L. Vincent,et al. Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.
[12] Alexander J. Smola,et al. Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.
[13] Henk Visscher,et al. Actual and theoretical gas consumption in Dutch dwellings: What causes the differences? , 2013 .
[14] Yu Zhang,et al. Design Considerations of a Centralized Load Controller Using Thermostatically Controlled Appliances for Continuous Regulation Reserves , 2013, IEEE Transactions on Smart Grid.
[15] Koen Vanthournout,et al. A Smart Domestic Hot Water Buffer , 2012, IEEE Transactions on Smart Grid.
[16] Antonio Criminisi,et al. Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..
[17] Ian Richardson,et al. Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .
[18] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[19] Andrew Kusiak,et al. Modeling and optimization of HVAC energy consumption , 2010 .
[20] Patrick D. McDaniel,et al. Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.
[21] Matthias Bitzer,et al. State estimation of a stratified storage tank , 2008 .
[22] Luis Pérez-Lombard,et al. A review on buildings energy consumption information , 2008 .
[23] J. K. Kok,et al. PowerMatcher: multiagent control in the electricity infrastructure , 2005, AAMAS '05.
[24] Gregor P. Henze,et al. Evaluation of Reinforcement Learning Control for Thermal Energy Storage Systems , 2003 .
[25] Abdullatif Ben-Nakhi,et al. Energy conservation in buildings through efficient A/C control using neural networks , 2002 .
[26] Nathan Intrator,et al. Boosted Mixture of Experts: An Ensemble Learning Scheme , 1999, Neural Computation.
[27] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .
[28] Ariel Rubinstein,et al. A Course in Game Theory , 1995 .
[29] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[30] G. Monahan. State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 1982 .