首页|King Abdulaziz University Researcher Highlights Research in Machine Learning (Ac curate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparativ e Study of Machine Learning Predictive Models and Feature Selection Techniques)
King Abdulaziz University Researcher Highlights Research in Machine Learning (Ac curate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparativ e Study of Machine Learning Predictive Models and Feature Selection Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; Current study results on artificial in telligence have been published. According to newsoriginating from Jeddah, Saudi Arabia, by NewsRx correspondents, research stated, “The growing interestin sol ar energy stems from its potential to reduce greenhouse gas emissions.”Funders for this research include Institutional Fund Projects.The news editors obtained a quote from the research from King Abdulaziz Universi ty: “Global horizontalirradiance (GHI) is a crucial determinant of the producti vity of solar photovoltaic (PV) systems.Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimizationof solar PV e nergy systems. This study evaluates the performance of six machine learning (ML) regressionmodels-artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR),Random Forest (RF), and support vector regress ion (SVR)-in predicting GHI for a site in northern SaudiArabia known for its hi gh solar energy potential. Using historical data from the NASA POWER database,c overing the period from 1984 to 2022, we employed advanced feature selection tec hniques to enhance thepredictive models. The models were evaluated based on met rics such as R-squared (R2), Mean SquaredError (MSE), Root Mean Squared Error ( RMSE), Mean Absolute Percentage Error (MAPE), and MeanAbsolute Error (MAE). The DT model demonstrated the highest performance, achieving an R2 of 1.0,MSE of 0 .0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0.”
King Abdulaziz UniversityJeddahSaudi ArabiaAsiaCyborgsEmerging TechnologiesMachine Learning
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