Long term forecasting using machine learning methods

Abstract

A robust model for power system load forecasting covering different horizons of time from short-term to long-term is an indispensable tool to have a better management of the system. However, as the horizon of time in load forecasting increases, it will be more challenging to have an accurate forecast. Machine learning methods have got more attention as efficient methods in dealing with the stochastic load pattern and resulting in accurate forecasting. In this study, the problem of long-term load forecasting for the case study of New England Network is studied using several commonly used machine learning methods such as feedforward artificial neural network, support vector machine, recurrent neural network, generalized regression neural network, k-nearest neighbors, and Gaussian Process Regression. The results of these methods are compared with mean absolute percentage error (MAPE).