Short Term Load forecasting System Based on Support Vector Kernel Methods

Load Forecasting is powerful tool to make important decisions such as to purchase and generate the electric power, load switching, development plans and energy supply according to the demand. The important factors for forecasting involve short, medium and long term forecasting. Factors in short term forecasting comprises of whether data, customer classes, working, non-working days and special event data, while long term forecasting involves historical data, population growth, economic development and different categories of customers. In this paper we have analyzed the load forecasting data collected from one grid that contain the load demands for day and night, special events, working and non-working days and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross validation 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 the techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.