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

Utpal Kumar Das, Kok Soon Tey, Mehdi Seyedmahmoudian, Mohd Yamani Idna Idris, Saad Mekhilef, Ben Horan and Alex Stojcevski
Additional contact information
Utpal Kumar Das: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Kok Soon Tey: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Mehdi Seyedmahmoudian: School of Engineering, Deakin University, Melbourne 3216, Australia
Mohd Yamani Idna Idris: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Saad Mekhilef: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Ben Horan: School of Engineering, Deakin University, Melbourne 3216, Australia
Alex Stojcevski: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria 3122, Australia

Energies, 2017, vol. 10, issue 7, 1-17

Abstract: 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.

Keywords: photovoltaic power forecasting; support vector regression; support vector machine; artificial neural network; different weather conditions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/7/876/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/7/876/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:7:p:876-:d:103113

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().