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SVR-based model to forecast PV power generation under different weather conditions

Das, Utpal Kumar, Tey, Kok Soon, Seyedmahmoudian, Mehdi, Idna Idris, Mohd Yamani, Mekhilef, Saad, Horan, Ben and Stojcevski, Alex 2017, SVR-based model to forecast PV power generation under different weather conditions, Energies, vol. 10, no. 7, pp. 1-17, doi: 10.3390/en10070876.

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Title SVR-based model to forecast PV power generation under different weather conditions
Author(s) Das, Utpal Kumar
Tey, Kok Soon
Seyedmahmoudian, Mehdi
Idna Idris, Mohd Yamani
Mekhilef, Saad
Horan, BenORCID iD for Horan, Ben orcid.org/0000-0002-6723-259X
Stojcevski, Alex
Journal name Energies
Volume number 10
Issue number 7
Article ID 876
Start page 1
End page 17
Total pages 17
Publisher Multidisciplinary Digital Publishing Institute
Place of publication Basel, Switzerland
Publication date 2017-07
ISSN 1996-1073
Keyword(s) Photovoltaic power forecasting
Support vector regression
Support vector machine
Artificial neural network
Different weather conditions
Summary 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.
Language eng
DOI 10.3390/en10070876
Field of Research 09 Engineering
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30099275

Document type: Journal Article
Collections: School of Engineering
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.