Assessing the relevance of load profiling information in electrical load forecasting based on neural network models

Sousa, J.C.; Neves, L.P.; H.M. Jorge

Publication

Assessing the relevance of load profiling information in electrical load forecasting based on neural network models

2012-09Journal article

datacite.subject.sdg	03:Saúde de Qualidade
datacite.subject.sdg	07:Energias Renováveis e Acessíveis
datacite.subject.sdg	11:Cidades e Comunidades Sustentáveis
dc.contributor.author	Sousa, J.C.
dc.contributor.author	Neves, L.P.
dc.contributor.author	H.M. Jorge
dc.date.accessioned	2025-11-24T16:52:57Z
dc.date.available	2025-11-24T16:52:57Z
dc.date.issued	2012-09
dc.description.abstract	The article is focused on evaluating the relevance of load profiling information in electrical load forecasting, using neural networks as the forecasting methodology. Different models, with and without load profiling information, were tested and compared, and, the importance of the different inputs was investigated, using the concept of partial derivatives to understand the relevance of including this type of data in the input space. The paper presents a model for the day ahead load profile prediction for an area with many consumers. The results were analyzed with a simulated load diagram (to illustrate a distribution feeder) and also with a specific output of a 60/15 kV real distribution substation that feeds a small town. The adopted methodology was successfully implemented and resulted in reducing the mean absolute percentage error between 0.5% and 16%, depending on the nature of the concurrent methodology used and the forecasted day, with a major benefit regarding the treatment of special days (holidays). The results illustrate an interesting potential for the use of the load profiling information in forecasting.	eng
dc.description.sponsorship	The authors gratefully acknowledge the contribution of EDP – Energias de Portugal in making available the consumption data of some local distribution substations for the sake of this study. This work has been partially supported by FCT through Projects PEst-C/EEI/UI0308/2011 and MIT/SET/0018/2009.
dc.identifier.citation	J.C. Sousa, L.P. Neves, H.M. Jorge, Assessing the relevance of load profiling information in electrical load forecasting based on neural network models, International Journal of Electrical Power & Energy Systems, Volume 40, Issue 1, 2012, Pages 85-93, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2012.02.008.
dc.identifier.doi	10.1016/j.ijepes.2012.02.008
dc.identifier.issn	0142-0615
dc.identifier.uri	http://hdl.handle.net/10400.8/14721
dc.language.iso	eng
dc.peerreviewed	yes
dc.publisher	Elsevier
dc.relation.hasversion	https://www.sciencedirect.com/science/article/pii/S014206151200035X
dc.relation.ispartof	International Journal of Electrical Power & Energy Systems
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/
dc.subject	Load forecast
dc.subject	Load profiling
dc.subject	Neural networks
dc.subject	Sensitivity analysis
dc.title	Assessing the relevance of load profiling information in electrical load forecasting based on neural network models	eng
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	93
oaire.citation.issue	1
oaire.citation.startPage	85
oaire.citation.title	International Journal of Electrical Power & Energy Systems
oaire.citation.volume	40
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	Sousa
person.familyName	Pires Neves
person.givenName	João
person.givenName	Luís
person.identifier.ciencia-id	C816-809B-4484
person.identifier.ciencia-id	591E-30D4-2C97
person.identifier.gsid	55971568600
person.identifier.orcid	0000-0002-7567-4910
person.identifier.orcid	0000-0002-2600-5622
person.identifier.scopus-author-id	34977315800
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