Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads

[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 .