期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2014
卷号：6
期号：3
页码：93
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Load Forecasting is powerful tool to make important decisions such as to purchase and generate theelectric power, load switching, development plans and energy supply according to the demand. Theimportant factors for forecasting involve short, medium and long term forecasting. Factors in short termforecasting comprises of whether data, customer classes, working, non-working days and special eventdata, while long term forecasting involves historical data, population growth, economic development anddifferent categories of customers.In this paper we have analyzed the load forecasting data collected fromone grid that contain the load demands for day and night, special events, working and non-working daysand different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold crossvalidation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against thetechniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using theSVM kernel shows that SVM MQ gives the highest performance of 99.53 %
关键词：Forecasting; Support Vector Machine (SVM); Linear Discriminant Analysis (LDA); QuadraticDiscriminant;Analysis (QDA); Mean Square Error (MSE); Relative Error (RE) and Mean Absolute Percentage Error;(MAPE); Cross Validation (CV)
