Recurrent Neural Networks Based Wind Speed Forecasting Models: A Case Study of Yalova

Abstract

Global warming and other adversarial effects caused by fossil fuel sources, renewable energy sources have been attracted more than ever. Especially, parties of Paris Climate Agreement countries pledge to reduce greenhouse gas emissions. Among renewable energy sources, wind energy is one of the significant and eligible source to produce energy sustainably. Wind energy is also one of the most important renewable energy source due to Turkey’s notable wind energy potential. Although wind energy is one of the most important clean energy sources, there are several challenges, such as intermittent and uncertain nature of wind places. Therefore, efficient and reliable energy planning and distribution mostly rely on prediction of wind energy with high accuracy. In this study, we propose four Reccurent Neural Network (RNN) methods to predict short-term wind energy production. We utilize data obtained from a station located in Yalova, Turkey to assess the performance of proposed algorithms. In our analysis, we plan to improve maintenance planning and intervene the sudden breakdowns by predicting 1 hour ahead energy production. First, we analyze the data received from the station, and the data sets were made suitable for the models. The performance results obtained from the models are plausible. Our results indicate that RNN methods can be successfully used to predict wind speed.