Recent advancements in solar energy forecasting have been highlighted in a new study published in the Journal of Big Data, led by Doaa El-Shahat from the Department of Computer Science at Zagazig University. As the world increasingly shifts towards renewable energy sources, particularly in light of the recent United Nations conference on climate change (COP28) in Dubai, accurate solar irradiance forecasting becomes crucial for optimizing power generation from photovoltaic (PV) systems.
Solar energy is a promising renewable resource, but its production is often hindered by the intermittent nature of sunlight. This variability can lead to disruptions in electricity generation, making reliable forecasting essential. The research focuses on comparing various machine learning (ML) and deep learning (DL) algorithms to improve short-term solar irradiance predictions. The dataset used for this study spans five years, from 2015 to 2019, and was collected in Islamabad using precise meteorological sensors.
One of the key innovations in this research is the application of Grid Search Cross Validation (GSCV) with five folds to optimize the hyperparameters of the models. This method enhances the performance of the algorithms, which are evaluated using several metrics, including the Adjusted R² score and Mean Absolute Error (MAE). Notably, the study found that the CNN-LSTM model achieved an impressive Adjusted R² score of 0.984, making it the most accurate among nine deep learning models tested. For machine learning methods, gradient boosting regression emerged as the leading technique with an Adjusted R² score of 0.962, outperforming six other models.
El-Shahat emphasized the importance of these findings, stating, “The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present.” This ability to predict solar energy availability can significantly enhance the reliability of solar power, making it a more attractive option for energy providers and investors.
The implications of this research are significant for the energy sector. Improved forecasting can lead to better energy management and integration of solar power into existing grids. Additionally, the use of explainable Artificial Intelligence (XAI) techniques, such as SHAP and LIME, allows stakeholders to understand the decision-making process of these models, fostering greater trust in their applications.
As countries and companies continue to invest in renewable energy infrastructure, the insights from this study can facilitate the transition away from fossil fuels. By enhancing the predictability of solar energy production, the research opens up commercial opportunities in energy storage, grid management, and the development of hybrid renewable energy systems. This study represents a critical step towards achieving a more sustainable energy future, reinforcing the role of solar power in the global energy mix.
