Research on Short-Term Low-Voltage Distribution Network Line Loss Prediction Based on Kmeans-LightGBM

• Published in: Journal of circuits, systems, and computers Vol. 31; no. 13
• Main Authors: Tang, Zhu, Xiao, Yuhang, Jiao, Yang, Li, Xinyu, Zhang, Caixia, Sun, Jun, Wang, Peng
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 15.09.2022; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Clustering; Electric potential; Electric power grids; Information systems; Quality assessment; Voltage; line loss; data quality evaluation system; Kmeans-LightGBM; big data platform; low-voltage distribution network
• DOI: 10.1142/S0218126622502280
• ISSN: 0218-1266

Due to the lack of data quality in real production environment, the traditional line loss calculation method cannot be applied, thus through the investigation of various information systems’ operation in power supply enterprises, a short-term low-voltage distribution network line loss prediction algorithm based on Kmeans-LightGBM is proposed. Operating data quality evaluation system of low-voltage distribution network was set up based on Hadoop platform, the feature dimensions were expanded by feature engineering, then those with no multicollinearity and high correlation with the line loss were selected, data normalization was again performed, Kmeans clustering algorithm was used to cluster the area and then, LightGBM algorithm was used to predict the classes within the area of line loss. Finally, the line loss of the numerical inverse normalization was found and validated with Beijing Power Grid of a low-voltage distribution network. By comparison, the model’s prediction accuracy is found to be higher than BPNN, FOA-SVR and traditional LightGBM.