Comparing different solutions for forecasting the energy production of a wind farm

Abstract

The production of different renewable and non-renewable energies sources can be coordinated efficiently to avoid costly overproduction. For that, it is important to develop models for accurate energy production forecasting. The energy production of wind farms is extremely dependent on the meteorological conditions. In this paper, computational intelligence techniques were used to predict the production of energy in a wind farm. This study is held on publicly accessible climacteric and energy data for a wind farm in Galicia, Spain, with 24 turbines of 9 different models. Data preprocessing was performed in order to delete outliers caused by the maintenance and technical problems. Models of the following types were developed: artificial neural networks, support vector machines and adaptive neuro-fuzzy inference system models. Furthermore, the persistence method was used as a time series forecast baseline model. Overall, the developed computational intelligence models perform better than the baseline model, being adaptive neuro-fuzzy inference system the model with the best results: a ~ 5% performance improvement over the baseline model.