Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10925380/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849767630222655488 |
|---|---|
| author | Da'ad Albahdal Maha Almousa Wijdan Aljebreen Aeshah A. Almutairi |
| author_facet | Da'ad Albahdal Maha Almousa Wijdan Aljebreen Aeshah A. Almutairi |
| author_sort | Da'ad Albahdal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6ac996b834574a6dbd4ec621eba88dcb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6ac996b834574a6dbd4ec621eba88dcb2025-08-20T03:04:07ZengIEEEIEEE Access2169-35362025-01-0113545855460010.1109/ACCESS.2025.355127110925380Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi ArabiaDa'ad Albahdal0https://orcid.org/0009-0002-7539-3059Maha Almousa1Wijdan Aljebreen2https://orcid.org/0009-0009-2809-7880Aeshah A. Almutairi3https://orcid.org/0000-0002-6313-727XDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaThis 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.https://ieeexplore.ieee.org/document/10925380/Renewable energybig datamachine learningsolar energyphotovoltaic cellsSaudi Arabia |
| spellingShingle | Da'ad Albahdal Maha Almousa Wijdan Aljebreen Aeshah A. Almutairi Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia IEEE Access Renewable energy big data machine learning solar energy photovoltaic cells Saudi Arabia |
| title | Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia |
| title_full | Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia |
| title_fullStr | Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia |
| title_full_unstemmed | Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia |
| title_short | Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia |
| title_sort | sunrise in the desert leveraging big data analytics for predictive solar energy production in saudi arabia |
| topic | Renewable energy big data machine learning solar energy photovoltaic cells Saudi Arabia |
| url | https://ieeexplore.ieee.org/document/10925380/ |
| work_keys_str_mv | AT daadalbahdal sunriseinthedesertleveragingbigdataanalyticsforpredictivesolarenergyproductioninsaudiarabia AT mahaalmousa sunriseinthedesertleveragingbigdataanalyticsforpredictivesolarenergyproductioninsaudiarabia AT wijdanaljebreen sunriseinthedesertleveragingbigdataanalyticsforpredictivesolarenergyproductioninsaudiarabia AT aeshahaalmutairi sunriseinthedesertleveragingbigdataanalyticsforpredictivesolarenergyproductioninsaudiarabia |