HOURLY GLOBAL SOLAR RADIATION ESTIMATION BASED ON MACHINE LEARNING METHODS IN ESKISEHIR

Year 2020, , 294 - 313, 15.06.2020

Massa Alsafadı , Ümmühan Başaran Filik

https://doi.org/10.18038/estubtda.650497

Cited By: 5

Abstract

Due to the increasing importance of knowing the global
solar radiation (GSR) amount incident on solar panels; short term data, such as
hourly global solar radiation (HGSR), is essentially required to obtain more accurate
and reliable power generation prediction. Nowadays, Machine Learning (ML)
methods are becoming a huge trend for data forecasting. Therefore, in this
paper, a comparison between Collares-Pereira & Rabl empirical model modified
by Gueymard (CPRG) and ML methods for HGSR estimation in Eskişehir city
in Turkey is conducted. Artificial Neural Network (ANN), Regression Tree (RT)
and Support Vector Regression (SVR) are ML methods that are used to predict
HGSR. Besides, hourly metrological and geographical parameters for the year
2014 are taken as inputs in the training models. The inputs are solar time,
solar hour angle, Julian day number, daily GSR, altitude, longitude, latitude, hourly
average humidity, hourly temperature and hourly pressure. To demonstrate these
techniques, a comparison is implemented using MATLAB software with the help of
existing toolboxes. Finally, this study proved that ML methods outperform the
CPRG model and not to mention that they have far more accurate results.
Although almost all ML models gave similar results, SVR
was the best among them with a correlation coefficient of 0.979532 for the
training set and 0.978244 for the testing set. In a nutshell, ML are successful
methods that should be to be taken into consideration to perfectly estimate
HGSR now and in the future in the field of solar renewable energy estimation.

Keywords

Hourly global solar radiation, Machine learning, Artificial neural network, Regression tree, Support vector regression.

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