Hourly Photovoltaics Power Output Prediction for Malaysia Using Support Vector Regression

Autor:	Kyairul Azmi Baharin, Hasimah Abd Rahman, Chin Kim Gan, Mohammad Yusri Hassan
Rok vydání:	2015
Předmět:	Weather monitoring Engineering Meteorology business.industry Photovoltaic system Irradiance General Medicine Solar energy Grid Support vector machine Photovoltaics Power output business Physics::Atmospheric and Oceanic Physics
Zdroj:	Applied Mechanics and Materials. 785:591-595
ISSN:	1662-7482
DOI:	10.4028/www.scientific.net/amm.785.591
Popis:	Reliable solar energy forecasting enables grid operators to manage the grid better as PV penetration level increases. This research explores the use of support vector regression to forecast hourly power output from a grid-connected PV system in Malaysia. Data is obtained from a grid-connected PV system that is equipped with a weather monitoring station. Three parameters are used as input to the forecast model; global irradiance, tilted irradiance and ambient temperature. Results were compared against a persistence model. The SVR model manages to forecast hourly power production with satisfactory accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bf9afb491af12cac679ee4383244c2a7 https://doi.org/10.4028/www.scientific.net/amm.785.591 Zobrazit plný text záznamu