Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia

Abstract

This research addresses the critical need for accurate solar energy forecasting in Saudi Arabia’s renewable energy transition. Leveraging the comprehensive Solar Resource Monitoring Stations dataset (2017–2021) from King Abdullah City for Atomic and Renewable Energy, this study presents the first extensive analysis of solar stations across Saudi Arabia with a systematic classification of solar stations into five distinct geographical groups, accounting for regional climate variations and solar radiation patterns. Using meteorological variables, including air conditions, wind patterns, relative humidity, and barometric pressure from 44 stations, we evaluated three machine learning models—Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT)—for predicting daily solar radiation. The results demonstrate that LR achieved superior performance with an RMSE of 139.71 and MAE of 98.32, significantly outperforming other models, particularly the Decision Tree model, which showed the highest error rate RMSE: 504.67. Through detailed regional analysis, identified northern and central regions exhibit consistently high Global Horizontal Irradiance (GHI), marking these areas as optimal locations for residential photovoltaic system deployment. These findings provide valuable insights for optimizing solar resource allocation and integration in Saudi Arabia’s power grid, facilitating the country’s transition to sustainable energy. This study’s results contribute to a better understanding solar resource distribution and variability, supporting informed decision-making for the Kingdom’s sustainable energy objectives.

Keywords

• Renewable energy
• big data
• machine learning
• solar energy
• photovoltaic cells
• Saudi Arabia