Controlling energy consumption in buildings through demand response (DR) has become increasingly important to reduce global carbon emissions and limit climate change. In this paper, we specifically focus on controlling the heating system of a residential building to optimize its energy consumption while respecting user's thermal comfort. Recent works in this area have mainly focused on either model-based control, e.g., model predictive control (MPC), or model-free reinforcement learning (RL) to implement practical DR algorithms. A specific RL method that recently has achieved impressive success in domains such as board games (go, chess) is Monte Carlo Tree Search (MCTS). Yet, for building control it has remained largely unexplored. Thus, we study MCTS specifically for building demand response. Its natural structure allows a flexible optimization that implicitly integrate exogenous constraints (as opposed, for example, to conventional RL solutions), making MCTS a promising candidate for DR control problems. We demonstrate how to improve MCTS control performance by incorporating a Physics-informed Neural Network (PiNN) model for its underlying thermal state prediction, as opposed to traditional purely data-driven Black-Box approaches. Our MCTS implementation aligned with a PiNN model is able to obtain a 3% increment of the obtained reward compared to a rule-based controller; leading to a 10% cost reduction and 35% reduction on temperature difference with the desired one when applied to an artificial price profile. We further implemented a Deep Learning layer into the Monte Carlo Tree Search technique using a neural network that leads the tree search through more optimal nodes. We then compared this addition with its Vanilla version, showing the improvement in computational cost required.

翻译：通过需求响应（DR）控制建筑能耗对于减少全球碳排放和应对气候变化日益重要。本文聚焦于控制住宅建筑的供暖系统，在保证用户热舒适度的前提下优化其能耗。该领域近期研究主要集中于基于模型的控制（如模型预测控制，MPC）或无模型强化学习（RL）来实施实用的DR算法。蒙特卡洛树搜索（MCTS）作为一种特定强化学习方法，近年来在棋类游戏（围棋、国际象棋）等领域取得了显著成功，但在建筑控制中的应用仍鲜有探索。为此，我们专门研究MCTS在建筑需求响应中的应用。其天然结构允许灵活优化，能隐式集成外部约束（与传统RL方案相比），使其成为DR控制问题的重要候选方案。我们证明通过引入物理信息神经网络（PiNN）模型进行底层热状态预测（而非传统纯数据驱动的黑箱方法），可提升MCTS控制性能。与基于规则控制器相比，结合PiNN模型的MCTS实现可获得3%的奖励提升；在人工电价场景下，成本降低10%，温度与目标值的偏差减少35%。我们进一步在蒙特卡洛树搜索技术中通过引导树搜索至更优节点的神经网络引入深度学习层，并与原始版本进行对比，展示了计算成本的改善效果。