Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms

Abstract

Wind power plays a critical role in supporting Ethiopia's electricity generation, particularly during dry seasons when hydropower availability diminishes. This contribution becomes even more crucial for sustaining industrial growth. However, accurately estimating wind energy remains challenging due to its random and highly variable nature. Deep learning models for time series prediction have been employed to address this issue, and this study evaluates the effectiveness of long short-term memory (LSTM) and gated recurrent unit (GRU) models in predicting wind speed at Adama Wind Farm II in Ethiopia. Model performance was assessed using R2, RMSE, and MAE. GRU significantly outperformed LSTM across all metrics and forecasting horizons. For daily forecasting, GRU achieved an R2 of 0.9727, exceeding LSTM's 0.9062 by 7.34 %, with corresponding RMSE values of 0.0005 and 0.0018, respectively. Similarly, GRU surpassed LSTM's performance for weekly and monthly forecasting. In weekly forecasting, GRU achieved an R2 of 0.9969 compared to LSTM's 0.9930, with RMSE values of 0.0001 for both models. For monthly forecasting, GRU achieved an R2 of 0.9330 compared to LSTM's 0.9303, with corresponding RMSE values of 0.0019 for both models. MAE values also followed the same pattern, with GRU consistently demonstrating lower values than LSTM across all forecasting horizons. These findings advance renewable energy and wind power forecasting research, highlighting the adaptability and versatility of LSTM and GRU models for capturing wind behavior. Decision-makers can use these findings to optimize wind power production, enhance grid operation efficiency, and promote sustainable and cost-effective wind energy. LSTM and GRU models have emerged as powerful tools for precise wind speed and wind power forecasting, which are essential for modern wind energy planning systems. They lay the foundation for a more environmentally friendly and sustainable energy future.

Keywords

• Deep learning
• LSTM and GRU models
• Renewable energy
• Wind speed forecasting
• Adama wind farm II
• Ethiopia