Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.
@article{arxiv.1805.03777,
title = {Deep Reinforcement Learning for Optimal Control of Space Heating},
author = {Adam Nagy and Hussain Kazmi and Farah Cheaib and Johan Driesen},
journal= {arXiv preprint arXiv:1805.03777},
year = {2018}
}
Comments
Accepted at Building Simulation and Optimization (BSO 2018), Cambridge, England