{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T05:25:57Z","timestamp":1775193957263,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2025R252"],"award-info":[{"award-number":["RSP2025R252"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Forecasting of time series data presents some challenges because the data\u2019s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data processing techniques with advanced machine learning algorithms to improve time series forecasting across the sustainability domain. Specifically, this study focuses on solar irradiation forecasting in Riyadh, Saudi Arabia. Efficient and accurate forecasts of solar irradiation are important for optimizing power production and its smooth integration into the utility grid. This advancement supports Saudi Arabia in Vision 2030, which aims to generate and utilize renewable energy sources to drive sustainable development. Therefore, the proposed forecasting system has been developed to the parameters characteristic of the Riyadh region environment, including high solar intensity, dust storms, and unpredictable weather conditions. After the cleaning and filtering process, the filtered dataset was pre-processed using the standardization method. Then, the Discrete Wavelet Transform (DWT) technique has been applied to extract the features of the pre-processed data. Next, the extracted features of the solar dataset have been split into three subsets: train, test, and forecast. Finally, two different machine learning techniques have been utilized for the forecasting process: Support Vector Machine (SVM) and Gaussian Process (GP) techniques. The proposed forecasting system has been evaluated across different time horizons: one-day, five-day, ten-day, and fifteen-day ahead. Comprehensive evaluation metrics were calculated including accuracy, stability, and generalizability measures. The study outcomes present the proposed forecasting system which provides a more robust and adaptable solution for time-series long-term forecasting and complex patterns of solar irradiation in Riyadh, Saudi Arabia.<\/jats:p>","DOI":"10.3390\/bdcc9020021","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T06:39:51Z","timestamp":1737959991000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Long-Term Forecasting of Solar Irradiation in Riyadh, Saudi Arabia, Using Machine Learning Techniques"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7960-5819","authenticated-orcid":false,"given":"Khalil","family":"AlSharabi","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5767-4741","authenticated-orcid":false,"given":"Yasser","family":"Bin Salamah","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-3440","authenticated-orcid":false,"given":"Majid","family":"Aljalal","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-2021","authenticated-orcid":false,"given":"Akram M.","family":"Abdurraqeeb","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia"}]},{"given":"Fahd A.","family":"Alturki","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/OAJPE.2020.3029979","article-title":"Energy Forecasting: A Review and Outlook","volume":"7","author":"Hong","year":"2020","journal-title":"IEEE Open Access J. 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