Publication:
A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

Simple item page
dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.affiliation.institutoUC3M. Instituto UC3M - Santander de Big Dataes
dc.contributor.authorAlonso Fernández, Andrés Modesto
dc.contributor.authorNogales Martin, Fco. Javier
dc.contributor.authorRuiz Mora, Carlos
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-07-28T08:46:19Z
dc.date.available2021-07-28T08:46:19Z
dc.date.issued2020-10-02
dc.descriptionThis article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence.en
dc.description.abstractMost electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.en
dc.description.sponsorshipThe authors gratefully acknowledge the financial support from the Spanish government through projects MTM2017-88979-P and PID2019-108311GB-I00/AEI/10.13039/501100011033, and from Fundación Iberdrola through “Ayudas a la Investigación en Energía y Medio Ambiente 2018”.en
dc.format.extent19
dc.identifier.bibliographicCitationAlonso, A. M., Nogales, F. J. & Ruiz, C. (2020). A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series. Energies, 13(20), 5328.en
dc.identifier.doihttps://doi.org/10.3390/en13205328
dc.identifier.issn1996-1073
dc.identifier.publicationfirstpage5328
dc.identifier.publicationissue20
dc.identifier.publicationtitleEnergiesen
dc.identifier.publicationvolume13
dc.identifier.urihttps://hdl.handle.net/10016/33161
dc.identifier.uxxiAR/0000027560
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDGobierno de España. MTM2017-88979-Pes
dc.relation.projectIDGobierno de España. PID2019-108311GB-I00es
dc.rights© 2020 by the authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherDisaggregated time seriesen
dc.subject.otherLoad forecastingen
dc.subject.otherNeural networksen
dc.subject.otherSmart metersen
dc.titleA Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Seriesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Single_Energies_2020.pdf
Size:
1.66 MB
Format:
Adobe Portable Document Format
Download
Collections
DES - Artículos de Revistas
USBDI - Artículos de Revistas