Powering Up: How a Unique Solar Radiation Prediction Model Outsmarts the Competition

As the world grapples with an energy crisis, predicting solar radiation (SR) accurately has become crucial for harnessing renewable energy. Machine Learning (ML) models have stepped up to tackle this challenge with impressive results. One such model, called the Cheetah Optimizer-Random Forest (CO-RF), is making waves. It uses the Cheetah Optimizer to pick the best features for hourly SR forecasts, which are then fed into the Random Forest model.

The CO-RF model was put to the test using two public SR datasets. Researchers checked its performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R2). The results showed that CO-RF outperformed other methods, including Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network, and even a standalone Random Forest. On the first dataset, CO-RF achieved a super low MAE of 0. 0365, MSE of 0. 0074, and an R2 of 0. 9251. On the second dataset, it had an MAE of 0. 0469, MSE of 0. 0032, and an R2 of 0. 9868. These numbers highlight a significant reduction in errors, proving that CO-RF is a top choice for predicting solar radiation.

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