A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand

Yuan, Yuxuan; Dehghanpour, Kaveh; Bu, Fankun; Wang, Zhaoyu

doi:10.1109/tpwrs.2020.2979943

Title: A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand

Journal Article · Tue Mar 10 00:00:00 EDT 2020 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2020.2979943· OSTI ID:1961209

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Iowa State Univ., Ames, IA (United States)

Advanced metering infrastructure (AMI) enables utilities to obtain granular energy consumption data, which offers a unique opportunity to design customer segmentation strategies based on their impact on various operational metrics in distribution grids. However, performing utility-scale segmentation for unobservable customers with only monthly billing information, remains a challenging problem. To address this challenge, we propose a new metric, the coincident monthly peak contribution (CMPC), that quantifies the contribution of individual customers to system peak demand. Furthermore, a novel multi-state machine learning-based segmentation method is developed that estimates CMPC for customers without smart meters (SMs): first, a clustering technique is used to build a databank containing typical daily load patterns in different seasons using the SM data of observable customers. Next, to associate unobservable customers with the discovered typical load profiles, a classification approach is leveraged to compute the likelihood of daily consumption patterns for different unobservable households. In the third stage, a weighted clusterwise regression (WCR) model is utilized to estimate the CMPC of unobservable customers using their monthly billing data and the outcomes of the classification module. Furthermore, the proposed segmentation methodology has been tested and verified using real utility data.

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Research Organization:: Iowa State Univ., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE); National Science Foundation (NSF)

Grant/Contract Number:: OE0000875; ECCS 1929975

OSTI ID:: 1961209

Journal Information:: IEEE Transactions on Power Systems, Vol. 35, Issue 5; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
Customer segmentation
peak load contribution
observability
machine learning

Title: A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand

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References (27)

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