Probabilistic reference model for hourly PV power generation forecasting
This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the...
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EDP Sciences
2020-01-01
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Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01002.pdf |
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doaj-63170be108664ed5abade7d34a336c542021-04-02T12:46:49ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011520100210.1051/e3sconf/202015201002e3sconf_peee2020_01002Probabilistic reference model for hourly PV power generation forecastingFernandez-Jimenez L. AlfredoTerreros-Olarte SoniaFalces AlbertoLara-Santillan Pedro M.Zorzano-Alba EnriqueZorzano-Santamaria Pedro J.This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01002.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fernandez-Jimenez L. Alfredo Terreros-Olarte Sonia Falces Alberto Lara-Santillan Pedro M. Zorzano-Alba Enrique Zorzano-Santamaria Pedro J. |
spellingShingle |
Fernandez-Jimenez L. Alfredo Terreros-Olarte Sonia Falces Alberto Lara-Santillan Pedro M. Zorzano-Alba Enrique Zorzano-Santamaria Pedro J. Probabilistic reference model for hourly PV power generation forecasting E3S Web of Conferences |
author_facet |
Fernandez-Jimenez L. Alfredo Terreros-Olarte Sonia Falces Alberto Lara-Santillan Pedro M. Zorzano-Alba Enrique Zorzano-Santamaria Pedro J. |
author_sort |
Fernandez-Jimenez L. Alfredo |
title |
Probabilistic reference model for hourly PV power generation forecasting |
title_short |
Probabilistic reference model for hourly PV power generation forecasting |
title_full |
Probabilistic reference model for hourly PV power generation forecasting |
title_fullStr |
Probabilistic reference model for hourly PV power generation forecasting |
title_full_unstemmed |
Probabilistic reference model for hourly PV power generation forecasting |
title_sort |
probabilistic reference model for hourly pv power generation forecasting |
publisher |
EDP Sciences |
series |
E3S Web of Conferences |
issn |
2267-1242 |
publishDate |
2020-01-01 |
description |
This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature. |
url |
https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01002.pdf |
work_keys_str_mv |
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1721567645676863488 |