The management of integrated energy systems in buildings is a challenging task that classical control approaches usually fail to address. The present paper analyzes the effect of the implementation of a reinforcement learning-based control strategy in an office building characterized by integrated energy systems with on-site electricity generation and storage technologies. The objective of the proposed controller is to minimize the operational cost to meet the cooling demand exploiting thermal energy storage and battery system considering a time-of-use electricity price schedule and local PV production. Two control solutions, a Soft-Actor-Critic agent coupled with a rule-based controller, and a fully rule-based control strategy, used as a baseline, are tested and compared considering various configurations of battery energy storage system capacities, and thermal energy storage sizes. Results show that the proposed control strategy leads to a reduction of operational energy costs respect to the fully rule-based control ranging from 39.5% and 84.3% among different configurations. Moreover the advanced control strategy improves the on-site PV utilization leading to an average increasing of self-sufficiency and self-consumption of 40% among different scenarios. The baseline control strategy results more sensitive to the size of storage whereas the proposed control achieves high savings also when smaller capacities of battery energy storage systems and sizes of thermal energy storage are implemented. The outcomes of the work prove the impact of implementation of advanced control as a way to optimize energy costs with a comprehensive view of the whole integrated energy system considering both thermal and electrical energy storage operation.

中文翻译：

---

用于优化建筑物综合能源系统管理的预测和自适应控制策略

建筑物中综合能源系统的管理是一项具有挑战性的任务，传统的控制方法通常无法解决。本文分析了在以具有现场发电和存储技术的综合能源系统为特征的办公楼中实施基于强化学习的控制策略的效果。所提出的控制器的目标是最大限度地降低运营成本，以满足利用热能存储和电池系统的冷却需求，考虑到使用时间电价表和本地光伏发电。考虑到电池储能系统容量和热能的各种配置，对两种控制解决方案（软演员评论家代理与基于规则的控制器相结合）以及完全基于规则的控制策略（用作基线）进行了测试和比较存储大小。结果表明，与完全基于规则的控制相比，所提出的控制策略在不同配置中可降低运营能源成本 39.5% 到 84.3%。此外，先进的控制策略提高了现场光伏利用率，不同场景下自给自耗平均提高40%。基线控制策略对存储的大小更加敏感，而当实施较小容量的电池储能系统和热能存储的大小时，所提出的控制也实现了高节省。这项工作的成果证明了实施先进控制作为优化能源成本的一种方式的影响，并全面考虑了考虑热能和电能存储运行的整个综合能源系统。