Short-Term Load Forecasting of building electricity consumption using NARX Neural Networks model
| dc.rights.license | open | en_US |
| dc.contributor.author | ZUAZO, Irati | |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | BOUSSAADA, Zina | |
| dc.contributor.author | AGINAKO, Naiara | |
| hal.structure.identifier | ESTIA INSTITUTE OF TECHNOLOGY | |
| dc.contributor.author | CUREA, Octavian
ORCID: 0000-0002-5030-2088 IDREF: 68259131 | |
| dc.contributor.author | CAMBLONG, Haritza | |
| dc.contributor.author | SIERRA, Basilio | |
| dc.date.accessioned | 2023-04-11T07:26:32Z | |
| dc.date.available | 2023-04-11T07:26:32Z | |
| dc.date.issued | 2021-09-08 | |
| dc.date.conference | 2021-09-08 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/172901 | |
| dc.description.abstractEn | Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective selfconsumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumptionforecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17,6% MAPE in NARX and 25,19% with LSTM model, both evaluated during a regular week on winter in adverse conditions . | |
| dc.language.iso | EN | en_US |
| dc.publisher | IEEE | en_US |
| dc.title.en | Short-Term Load Forecasting of building electricity consumption using NARX Neural Networks model | |
| dc.type | Autre communication scientifique (congrès sans actes - poster - séminaire...) | en_US |
| dc.identifier.doi | 10.23919/SpliTech52315.2021.9566440 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Autre | en_US |
| bordeaux.page | 1-6 | en_US |
| bordeaux.hal.laboratories | ESTIA - Recherche | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | Bordeaux Sciences Agro | en_US |
| bordeaux.conference.title | 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) | en_US |
| bordeaux.country | hr | en_US |
| bordeaux.conference.city | Bol and Split | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-03481377 | |
| hal.version | 1 | |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021-09-08&rft.spage=1-6&rft.epage=1-6&rft.au=ZUAZO,%20Irati&BOUSSAADA,%20Zina&AGINAKO,%20Naiara&CUREA,%20Octavian&CAMBLONG,%20Haritza&rft.genre=conference |
