Future Solar Irradiance Prediction Using Least Square Support Vector Machine

Fahteem Hamamy Anuwar, Ahmad Maliki Omar


Support vector machine (SVM) based on statistical learning theory has shown its advantage in regression and prediction. This paper presents the future prediction of the solar irradiance using least square support vector machine (LSSVM) which is a kind of SVM with quadric loss function. SVM has greater generalization ability and guarantee global minima for given training data set which will give good performance for solar irradiance with time series prediction. In order to improve the prediction performance of the LSSVM, the experimental data have to be normalized and appropriate parameters are selected by generic algorithm. In this research, solar irradiance data are collected daily at monitoring station located at Green Energy Research Centre (GERC) UiTM, Shah Alam. This related information will be used in prediction of the future data which useful for designing new PV systems and monitoring existing systems performance. The results show good agreement between the predicted against the actual values measured.  The proposed solar irradiance time series prediction method is considerable practical value which can be used in other datasets.


Solar irradiance; support vector machine; least square support vector machine; time series prediction

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DOI: http://dx.doi.org/10.18517/ijaseit.6.4.899


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Published by INSIGHT - Indonesian Society for Knowledge and Human Development