Research on Creating Multi-Attribute Power Consumption Behavior Portraits for Massive Users

Lü, Shixiang; Jiang, Haoran; Liu, Shengyuan; Xiaofeng, Feng; Li, Yang

doi:10.1109/icpesys.2018.8626971

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Thanks to the development of smart grid, IoTs applied in power industry can help operators to monitor and gain fine-grained data. Thus portrait techniques were gradually introduced to power industry, especially in the areas including outlier detection (Tang et al, 2014), equipment ledger management (Li et al, 2020), business management for commercial Internet platform of stateowned corporation (Yu et al, 2019), transmission line management (Zhang, 2019), electricity enterprise supplier management (Huang et al, 2021), user management (Shi et al, 2016;Wang et al, 2017;Feng et al, 2018;Lu et al, 2018;Zhong et al, 2018;ShiLu and Tian, 2020;Hu et al, 2021;Kong et al, 2021;Li et al, 2021;Yan et al, 2021). Users' tariff recovery risk, credit rating and energy efficiency rating was assessed with power consumption, payment and arrears data in (Shi et al, 2016;Wang et al, 2017;Yan et al, 2021) respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Users' tariff recovery risk, credit rating and energy efficiency rating was assessed with power consumption, payment and arrears data in (Shi et al, 2016;Wang et al, 2017;Yan et al, 2021) respectively. As for the power consumption behavior portrait, different indicators and different time-scale indicators of load characteristics were separately labeled for user portrait in (Lu et al, 2018) and (Hu et al, 2021). Beside of load data, massive user archives and consumption data were explored in (Feng et al, 2018) to understand users' consumption habits.…”

Section: Introductionmentioning

confidence: 99%

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Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion

Chen

Gan²,

Chen³

et al. 2022

Front. Energy Res.

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Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.

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