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【Applied Energy最新原创论文】多开放式办公室暖通系统的暖通空调系统的能源优化研究:一种深度强化学习方法

AEii国际应用能源  · 公众号  ·  · 2023-12-18 18:30

正文

原文信息:

Energy optimization for HVAC systems in multi-VAV open offices:A deepe reinforcement learning approache

原文链接:

https://www.sciencedirect.com/science/article/pii/S030626192301718X

Highlights

(1)      Model and analyze the thermodynamics of multi-zone open-plan offices

(2)      DRL-based control algorithm to optimize thermal comfort and energy efficiency

(3)      37% reduction in HVAC energy with less than 1% temperature violation

(4)      Smoothness-imposing reward function to minimize on-off transitions

(5)      Applicable to different floor plans under various weather conditions

摘要

随着全球变暖加剧和资源冲突升级,世界正在经历向可持续实践和节能解决方案的转型。商业和住宅建筑使用了全球超过32%的能源,迫切需要重新审视建筑能源管理(BEM)的传统方法。在bems平台中,调节供暖、通风和空调(HVAC)系统的运行更为重要,因为HVAC系统约占商业部门总能源成本的40%。

本文提出了一种深度强化学习(DRL)算法,作为一种数据驱动的方法来控制暖通空调的运行,以提高开放式办公商业建筑的能源效率,同时确保不同区域的居住者的热舒适。与基于规则的模型和模型预测控制等替代方法相比,数据驱动模型在优化建筑能耗方面显示出了很好的结果,而不需要特定于建筑的阈值、热量分布的潜在物理知识和气流的数字映射。尽管现代DRL方法在控制能源管理方面表现惊人,但对于具有多个可变风量(VAV)系统的开放式办公室,不同区域不能独立处理,仍然缺乏一种特殊的节能解决方案。此外,一些现有的方法存在一些关键问题,比如训练时间长,使用过于复杂的模型缺乏通用性,合并了难以建模和表征的外部因素,以及包括通常不可访问的因素。为了解决这些问题,我们提出了一种基于低复杂度drl的多输入多输出架构的开放式办公室暖通空调能量优化模型,该模型仅使用少量可控且可访问的因素。我们的解决方案的有效性是通过对总体能耗和热舒适水平的广泛分析来评估的,并与基于现有暖通空调时间表的基线系统进行了比较。这一对比表明,我们的方法在工作时间内以最低温度偏差(<1%)达到所需温度范围的情况下节省了37%的能耗。5个epoch(每个epoch约7.75 min)总共只需要40 min,就可以训练出性能优越、覆盖多种条件的网络,其架构复杂度较低;因此,它很容易适应建筑设置、天气条件、入住率等的变化。此外,通过加强控制策略的平滑性,我们抑制了暖通空调机组频繁和不愉快的开/关转换,以避免乘员不适和对系统的潜在损害。通过将该模型应用于不同的建筑模型和不同的天气条件,验证了该模型的通用性。

Abstr act

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. 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). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.

This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, 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 from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min 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.

Keywords

Smart buildings

Building energy management

Energy simulation

Energy optimization

Open-plan office

Deep reinforcement learning

HVAC system

Graphics


Fig. 1. (a) Final building energy consumption in the world by end-use in 2010 , and (b) the trend of energy used by different end-use sectors in the USA during the last 70 years .







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