Performance evaluation of seasonal solar irradiation models-case study: Karapinar town, Turkey

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs) and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapinar town Turkey. These methods, namely ANN, PSO and BT methods are widely used for solar irradiance estimation. The study utilizes input data from 2007 to 2020, including air temperature, precipitation, snow mass, air density, and cloud cover fraction, to estimate solar irradiance as the output value. The performance of these methods is analysed according to error indicators such as mean absolute error (MAE), root mean square error (RMSE) and R2. The results demonstrate that the BT method yields the lowest statistical error metrics. Additionally, the correlation matrix reveals a strong positive linear relationship (correlation coefficient of 0.9045) between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted via the employment of multiple linear regression (MLR) subsets and diverse combinations.

Automated summary

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs), and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapinar town Turkey. The study utilizes input data from 2007 to 2020 to estimate solar irradiance as the output value. The BT method yields the lowest statistical error metrics and there is a strong positive linear relationship between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted.