With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). With HVAC systems accounting for about 40% of the total energy cost in the commercial sector, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule in a real building. This comparison shows that our method achieves 37% savings in energy consumption with minimum violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 minutes for 5 epochs (about 7.75 minutes per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

翻译：全球超过32%的能源被商业和 residential 建筑消耗，这迫切要求我们重新审视传统的建筑能源管理方法。针对暖通空调系统占商业部门总能源成本约40%的现状，我们提出了一种低复杂度的深度强化学习模型，采用多输入多输出架构，仅利用少量可控且易获取的因素实现对开放式办公室的HVAC能耗优化。通过与真实建筑中基于现有HVAC调度方案的基线系统进行整体能耗和热舒适水平的全面比较分析，验证了我们方案的有效性。结果表明，我们的方法在工作时间内实现了37%的能耗节约，同时对目标温度范围的违反率极低（<1%）。该低复杂度架构的网络仅需总计40分钟（5个训练周期，约7.75分钟/周期）即可完成训练，并覆盖多种工况，因此能够轻松适应建筑布局、天气条件、占用率等变化。此外，通过强制控制策略的平滑性，我们抑制了HVAC设备频繁且令人不适的开关切换，从而避免降低 occupant 舒适度及潜在的系统损坏风险。通过将模型应用于不同建筑模型及多种天气条件，验证了其泛化能力。