Photovoltaic Energy Production Forecasting in a Short Term Horizon: Comparison between Analytical and Machine Learning Models
dc.contributor.author | Etxegarai Azkarategi, Garazi | |
dc.contributor.author | Zapirain Zuazo, Irati | |
dc.contributor.author | Camblong Ruiz, Aritza ![]() | |
dc.contributor.author | Ugartemendia de la Iglesia, Juan José ![]() | |
dc.contributor.author | Hernández, Juan | |
dc.contributor.author | Curea, Octavian | |
dc.date.accessioned | 2022-12-20T16:52:14Z | |
dc.date.available | 2022-12-20T16:52:14Z | |
dc.date.issued | 2022-11-28 | |
dc.identifier.citation | Applied Sciences 12(23) : (2022) // Article ID 12171 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10810/58954 | |
dc.description.abstract | The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R2 of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively. | es_ES |
dc.description.sponsorship | This research study carried out in the frame of the EKATE project has been supported by the FEDER Interreg POCTEFA program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | PV production forecasting | es_ES |
dc.subject | artificial intelligence | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | feedforward neural network | es_ES |
dc.subject | support vector regression | es_ES |
dc.subject | nonlinear autoregressive exogenous | es_ES |
dc.subject | OpenModelica | es_ES |
dc.subject | analytical model | es_ES |
dc.title | Photovoltaic Energy Production Forecasting in a Short Term Horizon: Comparison between Analytical and Machine Learning Models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-12-09T20:23:08Z | |
dc.rights.holder | © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/12/23/12171 | es_ES |
dc.identifier.doi | 10.3390/app122312171 | |
dc.departamentoes | Ingeniería eléctrica | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Ingeniaritza elektrikoa | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).