A comparison of machine learning algorithms for forecasting solar irradiance in Eskişehir, Turkey

Abstract

This work compares the efficiency of 45 different machine learning (ML) algorithms to provide a comprehensive and most accurate model for global horizontal solar irradiance (GHSI) prediction in Eskişehir, Turkey. The dataset is provided by NASA Prediction of Worldwide Energy Resource (POWER) as satellite data that involves some characteristic weather condition variables such as temperature, precipitation, humidity etc. over 35 years. Some ML algorithms such as Extra Trees, LightGBM, HistGB, Random Forest (RF), Bagging and Decision Tree exhibit better performance among the others with commonly used statistical evaluation metrics in literature such as coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In addition, Extra Tress regression slightly outperformed the rest of ensemble learning methods with R² of 0.99, RMSE of 8.05, MAE of 5.67, MAPE of 4%. Finally, the outcome demonstrates that the ML algorithms belonging to ensemble learning family achieved great results in GHSI prediction at specific location.

Predicting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta

“…They obtained results with an R value of 0.99. Ayko and Bozkurt Keser (2021), on the other hand, compared 45 different ML algorithms in their study. They found that ensemble learning methods outperformed other ML algorithms.…”