Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power

Mahdi Farhadi, Nader Mollayi


In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. 


Renewable Energy Sources; Solar photovoltaic energy; Forecasting of PV power generation; Supervised learning ; Least squares support vector machine

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ISSN 2088-8708, e-ISSN 2722-2578