Full metadata record
DC Field | Value | Language |
---|---|---|
dc.creator | Rodríguez, F. (Fermín) | - |
dc.creator | Martín, F. (Fernando) | - |
dc.creator | Fontán, L. (Luis) | - |
dc.creator | Galarza-Rodríguez, A. (Ainhoa) | - |
dc.date.accessioned | 2023-05-20T10:29:16Z | - |
dc.date.available | 2023-05-20T10:29:16Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Rodríguez, F. (Fermín); Martín, F. (Fernando); Fontán, L. (Luis); et al. "Very short-term load forecaster based on a neural network technique for smart grid control". Energies. 13 (19), 2020, 5210 | es |
dc.identifier.issn | 1996-1073 | - |
dc.identifier.uri | https://hdl.handle.net/10171/66303 | - |
dc.description.abstract | Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites. | es_ES |
dc.description.sponsorship | The authors would like to thank the Basque Government’s Department of Education for the financial support through the Researcher Formation Program—grant number PRE_2019_2_0035. This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 847054. The authors are grateful for the support and contributions of other members of the AmBIENCe project consortium, from VITO (Belgium), ENEA (Italy), TEKNIKER (Spain), INESC TEC (Portugal), ENERGINVEST (Belgium), EDP CNET (Portugal) and BPIE (Belgium). Further information can be found on the project website (http://ambience-project.eu/). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/847054/EU | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Smart grid | es_ES |
dc.subject | Energy demand | es_ES |
dc.subject | Very short-term forecaste | es_ES |
dc.title | Very short-term load forecaster based on a neural network technique for smart grid control | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.description.note | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.identifier.doi | 10.3390/en13195210 | - |
dadun.citation.number | 19 | es_ES |
dadun.citation.publicationName | Energies | es_ES |
dadun.citation.startingPage | 5210 | es_ES |
dadun.citation.volume | 13 | es_ES |
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