Wind Speed and Wind Power Forecasting using Statistical Models: AutoRegressive Moving Average (ARMA) and Artificial Neural Networks (ANN)

Abstract

Wind power, i.e., electrical energy produced making use of the wind resource, is being nowadays constantly connected to the electrical system. This has a non-negligible impact, raising issues like network stability and security of the supply. An accurate forecast of the available wind energy for the forthcoming hours is crucial, so that proper planning and scheduling of the conventional generation units can be performed. Also, with the liberalization of the electrical markets worldwide, the wind power forecasting reveals itself critical to assure that the bids are placed with a minimum possible risk. This work addresses the issue of forecasting wind power with two statistical models, the Autoregressive Moving Average and Artificial Neural Networks. The basic theory and the respective application of these models to perform wind power prediction are presented. Furthermore, their forecasting ability is compared in three different case studies. At the end, some conclusions are drawn about the performance of both models regarding its forecasting capabilities when compared with reference models.