Load and Price Forecasting in Smart Grids Using Enhanced Support Vector Machine

Abstract

In this paper, an enhanced model for electricity load and price forecasting is proposed. This model consists of feature engineering and classification. Feature engineering consists of feature selection and extraction. For feature selection a hybrid feature selector is used which consists of Decision Tree (DT) and Recursive Feature Elimination (RFE) to remove redundancy. Furthermore, Singular Value Decomposition (SVD) is used for feature extraction to reduce the dimensionality of features. To forecast load and price, two classifiers Stochastic Gradient Descent (SGD) and Support Vector Machine (SVM) is used and for better accuracy an enhanced framework of SVM is proposed. Dataset is taken from NYISO and month wise forecasting is being conducted by proposed classifiers. To evaluate performance RMSE, MAPE, MAE, MSE is used.