Enhanced Solar Irradiation Forecasting Using LSTM

Abstract

Accurate forecasting of solar irradiation is critical for improving grid stability, optimizing energy storage, and maximizing photovoltaic (PV) system efficiency. Traditional forecasting methods, including statistical and numerical weather prediction models, often struggle with the nonlinear and complex nature of solar irradiation data. This research work explores the application of deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, to enhance day-ahead solar irradiation forecasting for solar energy applications. In this research, an LSTM-based model was developed and trained using historical irradiation data, covering the period from January 1984 to March 2025 in Mekelle. The methodology involved data collection, cleaning, and preprocessing, followed by model training and evaluation. Key preprocessing steps included removing anomalies and normalizing the data set using Min-Max scaling. The LSTM model demonstrated superior performance compared to traditional machine learning models, with a Mean Bias Error (MBE) of 0 kWh/m²/day, Mean Absolute Error (MAE) of 0.46 kWh/m²/day, and Root Mean Squared Error (RMSE) of 0.65 kWh/m²/day. The model demonstrated a 71% lower RMSE in winter compared to summer and achieved 56% higher accuracy on sunny days than on cloudy days. These improvements can be attributed to the more stable weather conditions of the winter season and the consistency of solar irradiation on sunny days in Mekelle. The model was evaluated using two different dataset sizes, 5556 and 15040 data points. With the larger dataset, performance improved by 15%, highlighting the critical role of data availability in enhancing model accuracy and reliability. The results suggest the potential of LSTM networks in providing reliable day-ahead solar irradiation forecasts, contributing to the broader adoption of renewable energy. This study recommends integrating the solar irradiation forecasting model with energy forecasting systems to optimize grid performance and storage utilization. Future research should explore extended datasets, additional meteorological parameters, and ensemble methods to enhance the adaptability and accuracy of LSTM-based forecasting models