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Direct Solar Radiation Prediction using Multi-Model Evaluation with Trignometric Cyclic Process
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DOI：10.5937/fme2601128B.png)
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摘要
En 中文
Accurate prediction of renewable sources in general and solar radiation is critical for optimal integration of solar energy systems. The studyexplores eight Machine Learning models namely Linear Regression Model (LRM),Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR),Gaussian Process Regression (GPR), , Artificial Neural Network (ANN), k-Nearest Neighbors (NN), Support Vector Regression (SVR), and Deep Learning (DL) for predicting thedirect solar radiation atclimatically distinctsix sites in Saudi Arabia. Models are evaluated using eight statistical metrics along with time series and absolute error analyses. The present work introduced the Trigonometric Cyclical Encoding (TCE), which significantly improved the temporal learning. Comparative SHAPbased analysis revealed that TCE enhanced the explanatory power of temporal features by 49.26% and 53.40% for monthly and daily cycles. Resultsshow that DL achieved the lowest Root Mean Square Error (RMSE) and highest coefficient of determination, while ANN consistently indicated high accuracy at all thesites. Error and time series analyses denoted stable predictions byANN and DL; whereas LR, RFR, and k-Nearest Neighbors (NN) showed largerfluctuations. The proposed TCE technique additionally improved the model outputby maintaining the overall fitness of the models between 81.79% and 94.36% in all scenarios. This studyreinforcethe effec-tive planning of solar energy integration in differentclimatic conditions.
Keyword:
Forecasting
Renewable Energy
Solar Energy
Machine Learning
Deep Learning
Saudi Arabia
期刊
F
IF:
1.2
论文数:
17
被引数:
881
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