Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: 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 selfconsumption of the local photovoltaic (PV) production. A modelbased 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. Reinforcement Learning, Demand Response, Domestic Hot Water, Field Experiment March 1, 2017

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