Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques

Abstract

The exponential increase in electricity generation and consumption pattern are the two main issues in the wholesale markets. To handle these issues different machine learning techniques are used for load and price prediction in the research field. The wholesale utilities provide real-time data of load and price for the better prediction of electricity generation purposes. The New York Independent System Operator (NY-ISO) is one of the utility which provide electricity to different counties like United States, Canada and Israel. In this paper, hourly data of 2016–2017 is used for the forecasting process of load and price of New York City. Feature selection and extraction are used to achieve important features. The feature selection is done by two techniques Classification and Regression Tree (CART) and Recursive Feature Elimination (RFE) and Feature extraction by using Singular Value Decomposition (SVD). The Multiple Layer Perceptron (MLP), Support Vector Machine (SVM) and Logistic Regression (LR) classifiers are separately used for forecasting purposes of electricity load and price. Further enhance these three techniques EMLP, ESVM and ELR to take more accurate results for electricity load and price forecasting.