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