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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Main Authors: Fernandez-Jimenez L. Alfredo, Terreros-Olarte Sonia, Falces Alberto, Lara-Santillan Pedro M., Zorzano-Alba Enrique, Zorzano-Santamaria Pedro J.
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01002.pdf
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spelling 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
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