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Classical methods to control heating systems are often marred by suboptimalperformance, inability to adapt to dynamic conditions and unreasonableassumptions e.g. existence of building models. This paper presents a novel deepreinforcement learning algorithm which can control space heating in buildingsin a computationally efficient manner, and benchmarks it against other knowntechniques. The proposed algorithm outperforms rule based control by between5-10% in a simulation environment for a number of price signals. We concludethat, while not optimal, the proposed algorithm offers additional practicaladvantages such as faster computation times and increased robustness tonon-stationarities in building dynamics.

Deep Reinforcement Learning for Optimal Control of Space Heating