dc.creator | Parejo Matos, Antonio | es |
dc.creator | Bracco, Stefano | es |
dc.creator | Personal Vázquez, Enrique | es |
dc.creator | Larios Marín, Diego Francisco | es |
dc.creator | Delfino, Federico | es |
dc.creator | León de Mora, Carlos | es |
dc.date.accessioned | 2021-09-14T07:26:39Z | |
dc.date.available | 2021-09-14T07:26:39Z | |
dc.date.issued | 2021-07 | |
dc.identifier.citation | Parejo Matos, A., Bracco, S., Personal Vázquez, E., Larios Marín, D.F., Delfino, F. y León de Mora, C. (2021). Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models. Applied Sciences, 11 (14), 6420-. | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/125689 | |
dc.description.abstract | Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades (Government of Spain) project “Bigdata Analitycs e Instrumentación Cyberfísica para Soporte de Operaciones de Distribución en la Smart Grid” number RTI2018-094917-B-I00 | es |
dc.description.sponsorship | Ministerio de Educación y Formación Profesional, Government of Spain “Formación de Profesorado Universitario (FPU)” number FPU16/03522 | es |
dc.description.sponsorship | e Enel-Endesa Company “GRID Flexibility & Resilience Project” number P020-19/E24 | es |
dc.format | application/pdf | es |
dc.format.extent | 27 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciences, 11 (14), 6420-. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Short-term forecasting | es |
dc.subject | Multistep forecasting | es |
dc.subject | Artificial neural networks | es |
dc.subject | Renewable energy sources | es |
dc.subject | Smart grids | es |
dc.subject | Microgrids | es |
dc.title | Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.projectID | RTI2018-094917-B-I00 | es |
dc.relation.projectID | FPU16/03522 | es |
dc.relation.projectID | P020-19/E24 | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/11/14/6420 | es |
dc.identifier.doi | 10.3390/app11146420 | es |
dc.contributor.group | Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial | es |
idus.validador.nota | Mejor artículo del mes de julio de 2021 en Escuela Politécnica Superior, Universidad de Sevilla
Awarded as a best scientific publication of the month of July-2021 in Escuela Politécnica Superior, University of Seville. | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 14 | es |
dc.publication.initialPage | 6420 | es |
dc.description.awardwinning | Premio Mensual Publicación Científica Destacada de la US. Escuela Politécnica Superior | |