Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges
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Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges Longji Feng, Shu Xu, Linghao Zhang, Jing Wu, Jidong Zhang, Chengbo Chu, Zhenyu Wang and Haoyang Shi*
Abstract Driven by industrial development and the rising population, the upward trend of electricity consumption is not going to curb. While the electricity suppliers make every endeavor to satisfy the needs of consumers, they are facing the plight of indirect losses caused by technical or non-technical factors. Technical losses are usually induced by short circuits, power outage, or grid failures. The non-technical losses result from humans’ improper behaviors, e.g., electricity burglars. Due to the restrictions of the detection methods, the detection rate in the traditional power grid is lousy. To provide better electricity service for the customers and minimize the losses for the providers, a leap in the power grid is occurring, which is referred to as the smart grid. The smart grid is envisioned to increase the detection accuracy to an acceptable level by utilizing modern technologies, such as cloud computing. With the aim of obtaining achievements of anomaly detection for electricity consumption with cloud computing, we firstly introduce the basic definition of anomaly detection for electricity consumption. Next, we conduct the surveys on the proposed framework of anomaly detection for electricity consumption and propose a new framework with cloud computing. This is followed by centralized and decentralized detection methods. Then, the applications of centralized and decentralized detection methods for the anomaly electricity consumption are listed. Finally, the open challenges of the accuracy of detection and anomaly detection for electricity consumption with edge computing are discussed. Keywords: Smart grid, Cloud computing, Big data analysis, Anomaly detection
1 Introduction The development of the industry and the rise in population have increased the consumption of electricity. The upward trend of electricity consumption is not going to curb [1, 2]. While the electricity providers make every effort to fulfill the electricity consumption and to provide the best service to the customers, the service providers are suffering losses in technical and non-technical forms. Technical losses are usually caused by short circuits, power outage, or grid failures. Non-technical losses are *Correspondence: [email protected] Nanjing Power Supply Company, State Grid Jiangsu Electric Power Company, Nanjing 210019, China
mainly caused by humans’ inappropriate usage of electricity and electricity theft, etc. [3, 4]. In the USA alone, electricity theft was reported to cost the providers around $6B/year. Energy theft has been a serious problem in the traditional power system [5]. Identifying the non-technical factors and mitigating the losses incurred by them are the major concerns of the electricity service providers [6, 7]. The approach of anomaly detection in the traditional power gr
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