Comparison of the advanced machine learning methods for better prediction accuracy of solar radiation using only temperature data: A case study

Estimation of solar radiation (SR) carries importance for planning available renewable energy, and it is also beneficial for solving agricultural, meteorological, and engineering problems. This study compares the ability of hybrid adaptive neuro fuzzy (ANFIS) models and long short-term memory to search a suitable approach for SR prediction with minimum number of input parameters (temperature) in Mediterranean region of Turkey, which could be useful for the regions in which other effective parameters (eg, relative humidity, wind speed) are not available. The models considered were assessed by considering four data splitting scenarios, 50% train-50% test, 60% train-40% test, 70% train-30% test, and 80% train-20% test. Among the hybrid methods, the ANFIS with grey wolf optimization and genetic algorithm showed a superior accuracy. The study shows that applying different data splitting scenarios is necessary for better assessment of the data-driven methods since the accuracies of the implemented methods increase by about 30% to 60% when the splitting data scenario varies from 50-50% to 80-20%. Sensitivity analysis shows that the performance of the model increases by about 40% using extraterrestrial radiation for the best model. The ANFIS with grey wolf optimization and genetic algorithm is recommended to predict monthly solar radiation with limited input data.

Automated summary

This study compares the abilities of hybrid adaptive neuro fuzzy (ANFIS) models and long short-term memory (LSTM) models for predicting solar radiation with minimum input parameters. It is recommended to use different data splitting scenarios for better assessment of data-driven methods, and including extraterrestrial radiation can significantly improve model performance.