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dc.contributor.authorXie, Xiangen
dc.contributor.authorParlikad, Ajithen
dc.contributor.authorPuri, RSen
dc.date.accessioned2020-02-11T00:31:12Z
dc.date.available2020-02-11T00:31:12Z
dc.date.issued2019-10-01en
dc.identifier.isbn9781538680995en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301952
dc.description.abstractOver the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.
dc.description.sponsorshipThis research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (CSIC) and Centre for Digital Built Britain (CDBB) at the University of Cambridge.
dc.rightsAll rights reserved
dc.rights.uri
dc.titleA neural ordinary differential equations based approach for demand forecasting within power grid digital twinsen
dc.typeConference Object
prism.publicationDate2019en
prism.publicationName2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2019en
dc.identifier.doi10.17863/CAM.49029
dcterms.dateAccepted2019-07-10en
rioxxterms.versionofrecord10.1109/SmartGridComm.2019.8909789en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-10-01en
dc.contributor.orcidXie, Xiang [0000-0003-4601-9519]
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.funder-project-idEPSRC (EP/N021614/1)
pubs.funder-project-idEPSRC (EP/I019308/1)
pubs.funder-project-idEPSRC (EP/K000314/1)
pubs.funder-project-idEPSRC (EP/L010917/1)
cam.issuedOnline2019-11-25en
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8909789en
pubs.conference-nameIEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2019en
pubs.conference-start-date2019-10-21en
rioxxterms.freetoread.startdate2020-10-01


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