A review of electric load classification in smart grid environment

• Published in: Renewable & sustainable energy reviews Vol. 24; pp. 103 - 110
• Main Authors: Zhou, Kai-le, Yang, Shan-lin, Shen, Chao
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2013; Elsevier
• Subjects: Applied sciences; Clustering methods and result evaluation methods; Economic data; Electric energy; electricity; Energy; Energy economics; Exact sciences and technology; General, economic and professional studies; Load classification; Load classification applications; Methodology. Modelling; Process model; Smart grid; tariffs; Process model; Clustering methods and result evaluation methods; Load classification; Load classification applications; Smart grid
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2013.03.023

The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart grid environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented.