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24-Hour photovoltaic generation forecasting using combined very-short-term and short-term multivariate time series model
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Publication Year
2018-01-29
Journal
IEEE Power and Energy Society General Meeting
Publisher
IEEE Computer Society
Citation
IEEE Power and Energy Society General Meeting, Vol.2018-January, pp.1-5
Keyword
ASHRAE Clear-Sky ModelCombined ModelPhotovoltaic GenerationRenewable ForecastingShort-Term ForecastingVery-Short-Term Forecasting
Mesh Keyword
Clear sky modelsCombined modelPhotovoltaic generationShort-term forecastingVery short term forecasting
All Science Classification Codes (ASJC)
Energy Engineering and Power TechnologyNuclear Energy and EngineeringRenewable Energy, Sustainability and the EnvironmentElectrical and Electronic Engineering
Abstract
In order to achieve greenhouse gas reduction and renewable energy penetration target, photovoltaic generation can play an important role as an alternative to fossil fuel based generation in South Korea. However, due to its variability and uncertainty it is required to develop the model to forecast PV generation as accurately as possible. In this paper, a combined very-short-term and short-term model for 24-hour generation forecasting is proposed. Firstly, after considering weather factors affecting PV generation at the sample site in South Korea, the best single model for each time horizon is selected by the least forecasting error. Secondly, those are combined by the optimal value of forecasting time. As a result, the forecasting accuracy of the combined model is improved more than a single model for 24-hour generation forecasting.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36309
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046379286&origin=inward
DOI
https://doi.org/10.1109/pesgm.2017.8274605
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
Funding
ACKNOWLEDGMENT This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20162010103780).
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Jung, Jaesung  Image
Jung, Jaesung 정재성
Department of Electrical and Computer Engineering
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