High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies

Haipeng Pan, Zhongqian Yin and Xianzhi Jiang ()
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Haipeng Pan: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Zhongqian Yin: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Xianzhi Jiang: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China

Energies, 2022, vol. 15, issue 17, 1-14

Abstract: With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage behavior of users. The results show that the method is useful in establishing a real-time anomaly detection system in buildings, helping building managers to identify abnormal electricity usage by users. In addition, users can also use the system to understand their electricity usage and reduce energy waste.

Keywords: deep learning; anomaly detection; time series analysis; high-dimensional energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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