A New Hybrid Intelligent Method for Accurate Short Term Electric Power Production Forecasting From Uncertain Renewable Energy Resources

In recent years, there has been a significant increase in the utilization of renewable resources for electricity generation. Consequently, accurate short-term forecasting of renewable power production has become crucial for power system operations. However, Renewable Power Production Forecasting (RPPF) presents unique challenges due to the intermittent and uncertain nature of renewable energy sources. This paper proposes a novel approach to short-term RPPF. The proposed model integrates various techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The aim is to enhance the accuracy and predictive performance of renewable power production forecasts. The suggested hybrid model employs the Modified Relief-Mutual Information (MRMI) feature selection technique to identify the most influential input data for prediction. Subsequently, the combined model generates a 24-hour ahead RPP prediction using a weighted output approach. By capitalizing on the strengths of each individual model, the combined method mitigates their weaknesses, thereby improving the overall efficiency of the forecasting process. The accuracy and performance of the proposed method are evaluated through two case studies involving solar farm power generation at the Mahan, Iran and Rafsanjan, Iran sites. The results demonstrate the effectiveness of the hybrid model in enhancing the accuracy of short-term RPPF. By combining multiple forecasting methods and utilizing the MRMI feature selection technique, the proposed method significantly improves prediction accuracy.