Optimizing the input vectors of applied artificial neural network models for wind power production forecasting

Abstract

The significant increase of wind energy production worldwide revealed the necessity of its accurate forecasting. However, this is a very complex and, despite the progress made, more accurate forecasting methods are still needed. Accurate forecasts will contribute to a better power plant and grid management by solving problems related to the distribution and storage of the produced electricity, maximizing thus the profits of wind energy investments, contributing ultimately to their further enhancement. Here we present the development and validation of selected artificial neural network (ANN) wind energy forecasting models that produce hourly forecasts for 24 hours forecast horizon. The models are developed and validated using wind speed, direction, and energy produced by a wind power plant located at a semi-mountainous area in Western Greece. The ANN forecasts are compared against those of the persistence method using the Root Means Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) statistics.