Abnormal data detection method of energy consumption of green building power equipment based on LOF algorithm

Chao Li; Qi Wang; Yu Zhang

doi:10.1117/12.3026694

9 February 2024 Abnormal data detection method of energy consumption of green building power equipment based on LOF algorithm

Chao Li, Qi Wang, Yu Zhang

Author Affiliations +

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730X (2024) https://doi.org/10.1117/12.3026694
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Power equipment energy consumption data is affected by many factors, such as measurement errors, environmental changes, equipment aging, etc., these factors lead to data inaccuracy and noise interference. Therefore, it is difficult to detect the abnormal data. This paper presents a method for detecting abnormal energy consumption data of green building power equipment based on LOF algorithm. First, clean the energy consumption data of green building power equipment and collect it. The abnormal characteristics of power equipment energy consumption are obtained and standardized. Based on this, the Local Outlier Factor (LOF) algorithm is introduced to complete the accurate data detection of energy consumption anomalies of green building power equipment. The experimental results show that the detection accuracy of abnormal energy consumption data of power equipment in green buildings is ideal under the application of the research method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Li, Qi Wang, and Yu Zhang "Abnormal data detection method of energy consumption of green building power equipment based on LOF algorithm", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730X (9 February 2024); https://doi.org/10.1117/12.3026694

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