Clustering electricity consumers using high‐dimensional regression mixture models-Reference-Cited by-同舟云学术

Clustering electricity consumers using high‐dimensional regression mixture models

Published:2019-05-03 Issue:1 Volume:36 Page:159-177
ISSN:1524-1904
Container-title:Applied Stochastic Models in Business and Industry
language:en
Short-container-title:Appl Stochastic Models Bus Ind

Author:

Devijver Emilie¹,Goude Yannig²³,Poggi Jean‐Michel²⁴

Affiliation:

1. CNRS, Grenoble INP, LIGUniv. Grenoble Alpes Grenoble France

2. Laboratoire de Mathématiques d'Orsay (LMO)Université Paris‐Sud Orsay France

3. EDF R&DElectricité de France Palaiseau France

4. Université Paris Descartes Paris France

Funder

Belgian Federal Science Policy Office

Publisher

Wiley

Subject

Management Science and Operations Research,General Business, Management and Accounting,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asmb.2453

Reference40 articles.

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2. HeX HancherL AzevedoI KeyaertsN MeeusL GlachantJ‐M.Think Topic 11: Shift Not Drift: Towards Active Demand Response and Beyond. Technical Report.Florence Italy:Florence School Regulation European University Institute;2013.

3. KitayamaM MatsubaraR IzuiY.Application of data mining to customer profile analysis in the power electric industry. Paper presented at: IEEE Power Engineering Society Winter Meeting;2002;New York NY.

4. PrahastonoI KingD OzverenCS.A review of electricity load profile classification methods. Paper presented at: 42nd International Universities Power Engineering Conference;2007;Brighton UK.

5. GuardiaEC QueirozAR LimaJWM.Estimation of electricity elasticity for demand rates and load curve in Brazil. Paper presented at: IEEE Power and Energy Society General Meeting;2010;Providence RI.

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