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：全球商业和住宅建筑的能源消耗占比超过32%，因此亟需重新审视传统的建筑能源管理方法。鉴于暖通空调系统占商业领域总能源成本的约40%，我们提出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，仅利用少量可控且可获取的因素，对开放式办公室的暖通空调进行能源优化。通过将其实时建筑中现有暖通空调调度方案的基准系统进行全面能耗与热舒适度分析，验证了所提方案的有效性。对比结果表明，该方法在工作时间内实现了37%的能耗节省，同时温度波动超出目标范围的时长占比极低（<1%）。在低复杂度架构下，仅需40分钟即可完成5个训练周期（约每周期7.75分钟）的训练，获得覆盖多样化工况的高性能网络，因此可轻松适应建筑布局、气候条件、入住率等变化。此外，通过引入控制策略平滑性约束，有效抑制了暖通空调设备频繁且令人不适的启停切换，避免了居住者不适及系统潜在损伤。将该模型应用于不同建筑模型及多种气候条件，验证了其泛化能力。