Demand side management at district scale plays a crucial role in the energy transition process, being an ideal candidate to balance the needs of both users and grid, by managing the volatility of renewable sources and increasing energy flexibility. The presented study aims to explore the benefits of a coordinated approach for the energy management of a cluster of buildings to optimise the electrical demand profiles and provide services to the grid without penalising indoor comfort conditions. The proposed methodology makes use of a fully data-driven control scheme which exploits Long Short-Term Memory (LSTM) Neural Networks, and Deep Reinforcement Learning (DRL). A simulation environment is introduced to train a DRL controller to manage the operation of heat pumps and chilled and domestic hot water storage for a cluster of four buildings. LSTM models are trained with synthetic data set created in EnergyPlus and are integrated into simulation environment to evaluate the indoor temperature dynamics in each building. The developed DRL controller is tested against a manually optimised Rule Based Controller (RBC). Results show that the DRL algorithm is able to reduce the overall cluster electricity costs, while decreasing the peak energy demand by 23% and the Peak to Average Ratio (PAR) by 20%, without penalizing indoor temperature control.

地区规模的需求侧管理在能源转型过程中发挥着至关重要的作用，是通过管理可再生能源的波动性和提高能源灵活性来平衡用户和电网需求的理想选择。所提出的研究旨在探索协调方法对建筑群的能源管理的好处，以优化电力需求曲线并在不影响室内舒适条件的情况下向电网提供服务。所提出的方法利用完全数据驱动的控制方案，该方案利用了长短期记忆 (LSTM) 神经网络和深度强化学习 (DRL)。引入了一个模拟环境来训练 DRL 控制器来管理四栋建筑群的热泵以及冷冻和生活热水储存的运行。LSTM 模型使用在 EnergyPlus 中创建的合成数据集进行训练，并集成到模拟环境中以评估每个建筑物的室内温度动态。开发的 DRL 控制器针对手动优化的基于规则的控制器 (RBC) 进行测试。结果表明，DRL 算法能够降低整体集群电力成本，同时将峰值能量需求降低 23%，峰均比 (PAR) 降低 20%，而不会影响室内温度控制。