Hybrid Forecasting Model Integrating RNN-LSTM for Renewable Energy Production

Abstract

Renewable energy production forecasting is vital for effective grid management and energy trading. Traditional forecasting methods often fall short in capturing the complex and dynamic nature of renewable energy data. In this research, we propose a novel hybrid forecasting model that seamlessly integrates traditional electrical components with Recurrent Neural Networks using Long Short-Term Memory. Our model surpasses existing methods in forecasting accuracy, adaptability to changing conditions, and robustness to data quality. It provides valuable insights into long-term energy trends while reducing external dependencies. The hybrid model's success extends beyond renewable energy forecasting, offering a template for addressing forecasting challenges in various domains. Our findings and contributions advance the field, promising a future where accurate and adaptable renewable energy forecasts drive sustainability and innovation in the energy sector and beyond.