[论文解读] Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques

Ghezlane Halhoul Merabet, Mohamed Essaaidi+7 more|arXiv (Cornell University)|Apr 6, 2021

Building Energy and Comfort Optimization参考文献 192被引用 4

一句话总结

本篇系统性综述评估了人工智能辅助技术在智能建筑控制系统中的应用，重点关注暖通空调（HVAC）系统中热舒适性与能效之间的平衡。研究识别出优化、模式识别和预测控制是人工智能的关键功能，强调了高质量真实世界数据对提升性能的必要性，而建筑行业的数据限制导致性能仍不理想。

ABSTRACT

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector.

研究动机与目标

• 分析基于人工智能的建筑HVAC运行控制系统的研究现状。
• 识别用于平衡热舒适性与能效的关键人工智能技术。
• 评估这些人工智能技术在真实建筑应用中的性能与实施情况。
• 突出数据质量与可获取性方面的缺口，这些因素制约了人工智能模型在建筑能效系统中的有效性。

提出的方法

• 对2010年至2021年期间的人工智能辅助建筑控制系统进行了系统性文献综述。
• 将人工智能技术按功能分类，如模式识别、优化和预测控制。
• 基于其输出、实施方法以及在能耗与舒适度指标上的报告性能，评估了20种人工智能工具。
• 评估每种基于人工智能的解决方案在真实世界中的适用性及其数据需求。
• 综合分析当前人工智能在建筑控制中应用的优势、局限性及研究空白。
• 采用文献计量分析方法，绘制人工智能在建筑能效系统研究中的演变轨迹与研究重点。

实验结果

研究问题

• RQ1 在用于热舒适性与能效的智能建筑控制系统中，哪些人工智能技术被最常应用？
• RQ2 与传统方法相比，基于人工智能的控制系统在真实建筑环境中的表现如何？
• RQ3 影响人工智能模型在建筑能效系统中部署与性能的主要数据相关挑战是什么？
• RQ4 人工智能技术在不损害居住者热舒适性的情况下，能在多大程度上提升能效？
• RQ5 在人工智能驱动的建筑控制领域，特别是在数据质量和可扩展性方面，存在哪些关键研究空白与未来方向？

主要发现

• 在所审查的20种人工智能工具中，优化和预测控制是用于平衡能耗与热舒适性的最常用技术。
• 基于人工智能的系统在降低HVAC系统能耗方面展现出潜力，但不同研究之间的性能差异显著。
• 识别出的主要限制是缺乏高质量的真实世界数据，以有效训练和验证人工智能模型。
• 模式识别与异常检测功能被用于提升建筑运行系统的响应速度和故障检测能力。
• 尽管结果具有前景，但由于数据稀缺和实施复杂性，许多情况下基于人工智能的控制系统的性能仍不令人满意。
• 本综述强调了建筑与能源领域亟需标准化数据集和更完善的数据采集基础设施。