Enhancing Renewable Energy Forecasting using Roosters Optimization Algorithm and Hybrid Deep Learning Models

In recent years, there significant upsurge in the utilization of renewable resources for electricity generation. Consequently, accurate short-term predicting 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 study presents a deep learning-based approach for analyzing renewable energy consumption trends using a comprehensive dataset obtained from Kaggle. The dataset covers energy-related parameters from 175 countries over a 20-year period (2000–2020). To ensure data quality, preprocessing techniques such as missing data imputation, feature encoding, and Pearson correlation analysis were applied. The Roosters Optimization Algorithm (ROA) was employed for feature selection and hyperparameter tuning, optimizing computational efficiency and model performance. A hybrid Bidirectional Long-short term memory with Gated Recurrent unit (BLSTM-GRU) model was then implemented for classification, leveraging bidirectional dependencies and contextual information extraction for enhanced predictive accuracy. The proposed methodology effectively improves energy trend forecasting, demonstrating the potential of deep learning and bio-inspired optimization techniques in energy data analysis. Future work will explore additional optimization strategies and advanced neural architectures to further enhance model performance and generalizability.