Study of the SMO algorithm applied in power system load forecasting

Authors:
Jingmin Wang;Kanzhang Wu
Affiliations:
Department of Economy and Management, North China Electric Power University, Baoding;Department of Economy and Management, North China Electric Power University, Baoding
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 2
Cited 0

Fast training of support vector machines using sequential minimal optimization

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Abstract

A new methodology on the algorithm of sequential minimal optimization (SMO) for power system load was presented. In order to solve the problem that support vector machines (SVM) can not deal with large scale data, this paper introduces the modified algorithm of SMO to increase operational speed by use of a single threshold value. Adopting the actual data from the distribution network of a certain domestic city, and the load is forecasted by use of support vector regression (SVR) which is based on the modified SMO algorithm and proper kernel function. The forecasted results are compared with those SVR employing quadratic programming (QP) optimization algorithm and BP artificial neural method, and it is shown that the presented forecasting method is more accurate and efficient.