Abstract:

The maximum power load is an important part of power enterprise dispatching work. The accuracy of its prediction results will have an important impact on the distribution of power, the effective utilization rate of power, the quality of power supply service and the development of power system. Based on the 81-day maximum power load data of a city in Anhui province, 10 factors influencing the maximum power load of that day were selected, and the influencing factors reduced from 10 dimensions to 5 dimensions by kernel principal component analysis(KPCA). The cumulative contribution rate can reach 93.70%. The best parameters of SVM regression were selected by cross validation. 54 groups of data were randomly selected to train SVM prediction model. Finally, 27 groups of data were fitted and predicted. The mean square error of fitting prediction was 0.004 1, and the correlation coefficient was 0.963 1. The results show that the KPCA combining the SVM prediction model for maximum power load has good prediction ability.

Key words: power system, load, kernel principal component analysis (KPCA), support vector machine(SVM), prediction model