An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining. (April 2021)

• Record Type: Journal Article
• Authors: Xu, Yizhe; Yan, Chengchu; Shi, Jingfeng; Lu, Zefeng; Niu, Xiaofeng; Jiang, Yanlong; Zhu, Faxing
• Is Part Of: Sustainable energy technologies and assessments. Volume 44(2021)
• Publication Date: 2021-04
• Subjects: Fault detection -- Data mining -- Energy performance assessment -- Benchmarking; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101092
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: With the wide application of building automation systems (BASs), a large amount of building operation data are usually available, which provide a good basis for the optimal operation of a building's heating, ventilation and air conditioning (HVAC) systems. In this study, a data mining (DM)-based method is proposed for the anomaly detection and dynamic energy performance evaluation of an HVAC system. In this method, first a DM technology is used to detect the abnormal operation data from historical operation data and identify the possible reasons for abnormalities. Then, the identified abnormal energy consumption data caused by faults are corrected. On this basis, a multilevel dynamic energy performance benchmark and a set of energy performance evaluation rules for the HVAC system are established. Finally, the real-time operation performance of an HVAC system is evaluated, and the causes of abnormal energy consumption are identified at multiple levels. The effectiveness of the proposed method is verified in a case study of a commercial building with a complex cooling system.