TY  - JOUR
T1  - PREDICTION OF SOLAR RADIATION BASED ON MACHINE LEARNING METHODS
AU  - Hacioğlu, Rifat
PY  - 2017
DA  - June
JF  - The Journal of Cognitive Systems
JO  - JCS
PB  - İstanbul Technical University
WT  - DergiPark
SN  - 2548-0650
SP  - 16
EP  - 20
VL  - 2
IS  - 1
LA  - en
AB  - 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.
KW  - Solar radiation
KW  - prediction
KW  - linear regression
KW  - Gaussian process regression
KW  - machine learning
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CR  - S. Karasu, A. Altan, Z. Saraç, and R. Hacıoğlu, “Prediction of wind speed with non-linear autoregressive (NAR) neural networks”, IEEE 25th Signal Processing and Communications Applications Conference, 2017, pp. 1-4.
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UR  - https://dergipark.org.tr/en/pub/jcs/article/390233
L1  - https://dergipark.org.tr/en/download/article-file/419185
ER  -