Wind Energy Forecasting: A Comparative Study Between a Stochastic Model (ARIMA) and a Model Based on Neural Network (FFANN)

Abstract

The scope of this work is to analyze different time series modeling methods, reported in literature, for forecasting production of electrical energy from renewable energy sources such as wind energy. Two different models are considered: a stochastic model (ARIMA) and a model based on neural networks (FFANN or MLP). The studied time series represent the production of electrical energy from wind of a national power system during March 2018.