Classification of short-term loads of enterprises using LightGBM. Issue 1 (June 2020)

• Record Type: Journal Article
• Authors: Hao, Meiwei; Tian, Yuting; Gao, Jing; Wang, Yin; Tian, Runze
• Is Part Of: IOP conference series. Volume 526:Issue 1(2020)
• Publication Date: 2020-06
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/526/1/012179
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: As the society becomes increasingly energy-conscious these days, enterprises not only make a point of continuous supply for power, but try to improve power utilization efficiency and design scientific power use plans. In this study, the LightGBM algorithm was used to classify the short-term loads of enterprises, and the characteristics of short-term loads were analyzed to realize accurate classification. The research results will be instrumental to the enterprises' scientific scheduling of power use, thereby improving safety and reliability of the power grid, and providing a strong basis for future grid planning.