Enhancing Photovoltaic Power Forecasting through Hybrid Deep Learning Models: A CNN-RNN Approach for Grid Stability and Renewable Energy Optimization

Abstract

This paper addresses the critical need for accurate photovoltaic (PV) power generation predictions to ensure efficient grid integration and management, especially considering the variability and intermittency of solar power. By exploring advanced deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and a hybrid CNN-RNN model, the study aims to enhance the accuracy and reliability of solar power forecasts. The CNN model achieved an accuracy of 0.84, while the RNN reached 0.94, with the highest accuracy of 0.99 attained by the hybrid CNN-RNN model. These models provide vital tools for mitigating fluctuations in solar power output, improving grid stability, and optimizing energy distribution. The study contributes to the advancement of renewable energy forecasting, helping to ensure a more sustainable and reliable energy future, while also supporting efforts to reduce CO2 emissions and combat climate change.

Keywords

• photovoltaic power generation
• artificial intelligence
• deep neural networks
• recurrent neural networks
• bidirectional long short-term memory
• convolutional neural networks