Solar Irradiance Forecasting Using Deep Learning Techniques

Abstract

Solar irradiance, the power of sunlight received on a given surface area during a specific time, is crucial in determining the efficiency and performance of solar power systems, as it directly influences the electricity units generated by photovoltaic (PV) cells. In recent years, deep learning and machine learning techniques have been leveraged to enhance the accuracy of solar adsorption and wind power forecasting. In this context, this study presents a comparative study of various deep learning models for very short term solar irradiance forecasting, aiming to find the most effective model for this specific purpose for our local city Karachi. The key findings indicate that the LSTM model outperforms the other architectures, achieving the highest R-squared value and the lowest RMSE. These results emphasize the importance of accurate forecasting models in optimizing renewable energy generation and grid management and their potential applications in various sectors.

Keywords

• solar irradiance
• comparative analysis
• deep learning
• time series forecasting
• karachi dataset
• recurrent neural networks