Short-Term Electricity Price and Load Forecasting using Enhanced Support Vector Machine and K-Nearest Neighbor

Abstract

Accurate load and price forecasting is one of the crucial stage in Smart Grid (SG). An efficient load and price forecasting is required to minimize the large difference among power generation and consumption. Accurate selection and extraction of meaningful features from data are challenging. In this paper, New York Independent System Operator (NYISO) six months' load and price data is used for forecasting. Decision Tree (DT) is used for feature selection and Recursive Feature Elimination (REF) technique is used for feature extraction. REF technique is used to remove redundancy from selected features. After feature extraction, two classifiers are used for forecasting. One classifier is Support Vector Machine (SVM) and other classifier is K-Nearest Neighbor (KNN). These classifiers have different parameters with some default values. Week ahead load and price forecasting is performed in this work. Accuracy of modified SVM is 89.5984% and modified KNN is 89.8605% is achieved for load forecasting. For price, accuracy of modified SVM is 88.2740% and modified KNN is 85.5999%.