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Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration

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Authors: Oscar De Somer,Ana Soares,Tristan Kuijpers,Koen Vossen,Koen Vanthournout,Fred Spiessens
ArXiv: 1703.05486
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1703.05486v1


This paper demonstrates a data-driven control approach for demand response in real-life residential buildings. The objective is to optimally schedule the heating cycles of the Domestic Hot Water (DHW) buffer to maximize the self-consumption of the local photovoltaic (PV) production. A model-based reinforcement learning technique is used to tackle the underlying sequential decision-making problem. The proposed algorithm learns the stochastic occupant behavior, predicts the PV production and takes into account the dynamics of the system. A real-life experiment with six residential buildings is performed using this algorithm. The results show that the self-consumption of the PV production is significantly increased, compared to the default thermostat control.

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