Solar Power Prediction via Support Vector Machine and Random Forest

Solar Power Prediction via Support Vector Machine and Random Forest

Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainf...

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Main Authors: Yen Chih-Feng, Hsieh He-Yen, Su Kuan-Wu, Yu Min-Chieh, Leu Jenq-Shiou
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20186901004
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spelling doaj-dafc1c21df7b45268692775b9293cc5f2021-04-02T10:26:07ZengEDP SciencesE3S Web of Conferences2267-12422018-01-01690100410.1051/e3sconf/20186901004e3sconf_gesg2018_01004Solar Power Prediction via Support Vector Machine and Random ForestYen Chih-Feng0Hsieh He-Yen1Su Kuan-Wu2Yu Min-Chieh3Leu Jenq-Shiou4Department of Electronic and Computer Engineering, National Taiwan University of Science and TechnologyDepartment of Electronic and Computer Engineering, National Taiwan University of Science and TechnologyDepartment of Electronic and Computer Engineering, National Taiwan University of Science and TechnologyDepartment of Electronic and Computer Engineering, National Taiwan University of Science and TechnologyDepartment of Electronic and Computer Engineering, National Taiwan University of Science and TechnologyDue to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).https://doi.org/10.1051/e3sconf/20186901004
collection DOAJ
language English
format Article
sources DOAJ
author Yen Chih-Feng
Hsieh He-Yen
Su Kuan-Wu
Yu Min-Chieh
Leu Jenq-Shiou
spellingShingle Yen Chih-Feng
Hsieh He-Yen
Su Kuan-Wu
Yu Min-Chieh
Leu Jenq-Shiou
Solar Power Prediction via Support Vector Machine and Random Forest
E3S Web of Conferences
author_facet Yen Chih-Feng
Hsieh He-Yen
Su Kuan-Wu
Yu Min-Chieh
Leu Jenq-Shiou
author_sort Yen Chih-Feng
title Solar Power Prediction via Support Vector Machine and Random Forest
title_short Solar Power Prediction via Support Vector Machine and Random Forest
title_full Solar Power Prediction via Support Vector Machine and Random Forest
title_fullStr Solar Power Prediction via Support Vector Machine and Random Forest
title_full_unstemmed Solar Power Prediction via Support Vector Machine and Random Forest
title_sort solar power prediction via support vector machine and random forest
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2018-01-01
description Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).
url https://doi.org/10.1051/e3sconf/20186901004
work_keys_str_mv AT yenchihfeng solarpowerpredictionviasupportvectormachineandrandomforest
AT hsiehheyen solarpowerpredictionviasupportvectormachineandrandomforest
AT sukuanwu solarpowerpredictionviasupportvectormachineandrandomforest
AT yuminchieh solarpowerpredictionviasupportvectormachineandrandomforest
AT leujenqshiou solarpowerpredictionviasupportvectormachineandrandomforest
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