Probabilistic peak load estimation in smart cities using smart meter data

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Title: Probabilistic peak load estimation in smart cities using smart meter data
Authors: Sun, M
Wang, Y
Strbac, G
Kang, C
Item Type: Journal Article
Abstract: Adequate capacity planning of substations and feeders primarily depends on an accurate estimation of the future peak electricity demand. Traditional coinci- dent peak demand estimation is carried out based on the empirical metric, after diversity maximum demand (ADMD), indicating individual peak consumption levels and of de- mand diversification across multiple residents. With the privilege of smart meters in smart cities, this paper pro- poses a data-driven probabilistic peak demand estima- tion framework using fine-grained smart meter data and socio-demographic data of the consumers, which drive fundamental electricity consumptions across different cat- egories. In particular, four main stages are integrated in the proposed approach: load modeling and sampling via the proposed variable truncated R-vine copulas (VTRC) method; correlation-based customer grouping; probabilis- tic normalized maximum diversified demand (NMDD) esti- mation; and probabilistic peak demand estimation for new customers. Numerical experiments have been conducted on real demand measurements across 2,639 households in London, collected from Low Carbon London (LCL) projects smart-metering trial. The mean absolute percentage error (MAPE) and pinball loss function are used to quantitatively demonstrate the superiority of the proposed approach in terms of the point estimate value and the probabilistic result, respectively.
Issue Date: 1-Feb-2019
Date of Acceptance: 25-Jan-2018
URI: http://hdl.handle.net/10044/1/56674
DOI: https://dx.doi.org/10.1109/TIE.2018.2803732
ISSN: 0278-0046
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1608
End Page: 1618
Journal / Book Title: IEEE Transactions on Industrial Electronics
Volume: 66
Issue: 2
Copyright Statement: © 2018 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor/Funder: London Power Networks PLC
Engineering & Physical Science Research Council (EPSRC)
EPSRC
Innovate UK
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Funder's Grant Number: PO No: 4520618760
EP/I038837/1
EP/I038837/1
102228
EP/N030028/1
PO: 5510854 - WVR3114N
Keywords: Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Instruments & Instrumentation
Engineering
Coincident peak demand
distribution network planning
probabilistic estimation
R-vine copulas
smart meter
ELECTRICITY
REGRESSION
DEMAND
08 Information And Computing Sciences
09 Engineering
Electrical & Electronic Engineering
Publication Status: Published
Online Publication Date: 2018-02-28
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences



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