Classification of daily load characteristics curve and forecasting of short-term load based on fuzzy clustering and improved BP algorithm

• Published in: Dianli Xitong Baohu yu Kongzhi Vol. 40; no. 3; pp. 56 - 60
• Main Authors: Li, Zuo, Zhou, Bu-Xiang, Lin, Nan
• Format: Journal Article
• Language: Chinese
• Published: 01.02.2012
• Subjects: Algorithms; Back propagation; Classification; Forecasting; Fuzzy; Fuzzy logic; Fuzzy set theory; Mathematical models
• ISSN: 1674-3415

A classification of daily load characteristics curve method and forecasting of short-term load which combines fuzzy clustering with one of the artificial neural network named BP neural is put forward. Different typical load curves are created by means of fuzzy clustering technology which classifies the load characteristics curves of different customers. Then this paper takes use of the typical curve which is similar to the predicted curve, and associates with some factors which exert greater influence on short-term load forecasting, such as temperature, day type and humidity and so on to build relevant BP neural model. Aiming at the shortage of BP algorithm, the variable learning rate and the additional momentum are adopted to improve BP algorithm and predict daily load curve. The classification of practical daily load curve samples and forecasting of short-term load prove that the proposed method possesses higher forecasting precision, having the feasibility in practical application.