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Hourly photovoltaics power output prediction for Malaysia using support vector regression

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 sys...

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Main Authors: Baharin, Kyairul Azmi, Abdul Rahman, Hasimah, Hassan, Mohammad Yusri, Chin, Kim Gan
Format: Conference or Workshop Item
Published: 2015
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/61458/
https://eventegg.com/peoco-2015/
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id my.utm.61458
record_format eprints
spelling my.utm.614582017-08-07T00:36:54Z http://eprints.utm.my/id/eprint/61458/ Hourly photovoltaics power output prediction for Malaysia using support vector regression Baharin, Kyairul Azmi Abdul Rahman, Hasimah Hassan, Mohammad Yusri Chin, Kim Gan TJ Mechanical engineering and machinery 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. 2015 Conference or Workshop Item PeerReviewed Baharin, Kyairul Azmi and Abdul Rahman, Hasimah and Hassan, Mohammad Yusri and Chin, Kim Gan (2015) Hourly photovoltaics power output prediction for Malaysia using support vector regression. In: 2015 9th International Power Engineering and Optimization Conference, 18-19 Mar, 2015, Melaka, Malaysia. https://eventegg.com/peoco-2015/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Baharin, Kyairul Azmi
Abdul Rahman, Hasimah
Hassan, Mohammad Yusri
Chin, Kim Gan
Hourly photovoltaics power output prediction for Malaysia using support vector regression
description 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.
format Conference or Workshop Item
author Baharin, Kyairul Azmi
Abdul Rahman, Hasimah
Hassan, Mohammad Yusri
Chin, Kim Gan
author_facet Baharin, Kyairul Azmi
Abdul Rahman, Hasimah
Hassan, Mohammad Yusri
Chin, Kim Gan
author_sort Baharin, Kyairul Azmi
title Hourly photovoltaics power output prediction for Malaysia using support vector regression
title_short Hourly photovoltaics power output prediction for Malaysia using support vector regression
title_full Hourly photovoltaics power output prediction for Malaysia using support vector regression
title_fullStr Hourly photovoltaics power output prediction for Malaysia using support vector regression
title_full_unstemmed Hourly photovoltaics power output prediction for Malaysia using support vector regression
title_sort hourly photovoltaics power output prediction for malaysia using support vector regression
publishDate 2015
url http://eprints.utm.my/id/eprint/61458/
https://eventegg.com/peoco-2015/
_version_ 1643655172240441344
score 13.2014675

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