SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

Das, U.K. and Tey, Kok Soon and Seyedmahmoudian, M. and Idris, Mohd Yamani Idna and Mekhilef, Saad and Horan, B. and Stojcevski, A. (2017) SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions. Energies, 10 (7). p. 876. ISSN 1996-1073, DOI

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Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.

Item Type: Article
Funders: Fundamental Research Grant Scheme (FRGS), Project No: FP061-2016
Uncontrolled Keywords: Photovoltaic power forecasting; Support vector regression; Support vector machine; Artificial neural network; Different weather conditions
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Sep 2018 04:12
Last Modified: 25 Oct 2019 05:37

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