Deep Reinforcement Learning for Optimal Control of Space Heating
This work addresses inefficient heating control in buildings, offering incremental improvements in computational efficiency and robustness.
The paper tackled the problem of suboptimal and inflexible control of space heating systems by proposing a deep reinforcement learning algorithm, which outperformed rule-based control by 5-10% in simulations for various price signals.
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.