PREDICTION OF SOLAR RADIATION BASED ON MACHINE LEARNING METHODS

Year 2017, Volume: 2 Issue: 1, 16 - 20, 15.06.2017

Rifat Hacioğlu

Abstract

In this study, machine learning methods which linear regression and Gaussian process regression models are used to estimate the solar radiation on daily data set taken from the wind central in Zonguldak province in Turkey. The measured wind speed, temperature, pressure, humidity parameters together with solar radiation are used for the prediction process. In the prediction process, number of delay steps from 3 to 12 for these parameters are applied to the developed models. In order to determine the performance of the obtained model, the model is evaluated in terms of statistical error criteria such as MAE, MSE and RMSE. The least prediction error for the solar radiation prediction process is determined. It has been observed that Gaussian regression model approach provides a high performance to predict solar radiation with related to other measured parameters. 

Keywords

Solar radiation, prediction, linear regression, Gaussian process regression, machine learning

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