Discriminant analysis classification of residential electricity smart meter data

Neale, Adam; Kummert, Michaël; Bernier, Michel

doi:10.1016/j.enbuild.2021.111823

Cited by 13 publications

(8 citation statements)

References 22 publications

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“…Among them, two kinds of equipment terminals, the dedicated terminal and the public terminal, are selected to complete the collection of electric energy metering data for users with large power load and low voltage respectively; RS485 line is selected as the direct connection between the dedicated terminal and the threephase electric energy meter, which can read the current and voltage metering data in real time and also collect a small amount of electric energy metering data. For the real-time situation of power factor, current, voltage, active and reactive power, power of each rate and positive and reverse, due to the large number of single-and three-phase power meters of low-voltage users, the power line carrier method is used through the public transformer terminal, and the power of positive and reverse and each rate needs to be transcribed to the main station of the collection system [5,6].…”

Section: Abnormal Identification Of Power Metering Data In Shared Pow...mentioning

confidence: 99%

Method for identifying abnormal power metering data in shared power information collection system

Li,

Miao,

Jing

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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Section: Abnormal Identification Of Power Metering Data In Shared Pow...mentioning

confidence: 99%

Method for identifying abnormal power metering data in shared power information collection system

Li,

Miao,

Jing

et al. 2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. meters are the key device to connect customers and the grid, and they are an important interface in understanding customers' electricity consumption (Humeres, 2022;Neale et al, 2022). How to use the abundance of customer electricity consumption data collected by smart meters and data mining them to obtain effective information that can help improve operational reliability and economic and social benefits.…”

Section: Declaration Of Interestsmentioning

confidence: 99%

“…According to a report issued by Wood Mackenzie, the total installation volume of global smart meters is expected to increase from 665.1 billion units in 2017 to more than 1.2 billion units at the end of 2024 (Shi and Jiao, 2023). As an important part of the advanced measurement system of the smart grid, smart meters are the key device to connect customers and the grid, and they are an important interface in understanding customers' electricity consumption (Humeres, 2022; Neale et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

Pan,

Liu

2023

MEQ

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PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.Design/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.FindingsCompared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.Originality/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.HighlightsThe stability of electricity consumption is important to the stable operation of the grid.An improved FP-growth algorithm is employed to explore the influencing factors.The improved algorithm enables the mining of rules containing specific attribute labels.Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

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“…It is this process that ensures that the cost of the renovation will be cost-effective. In order to perform the calibration, the use of the smart meter is essential, as it allows the actual consumption data to be matched with the simulation at an hour by hour resolution [68]. While not all homes in the EU have a smart meter installed, their implementation is within the commitments made by MS (80% of residential buildings must have smart meters by 2020).…”

Section: Study Limitationsmentioning

confidence: 99%

Evaluation of cost-effective measures for the renovation of existing dwellings in the framework of the energy certification system: A case study in Norway

Caceres

Karlshøj²,

Vik

et al. 2022

Energy and Buildings

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Discriminant analysis classification of residential electricity smart meter data

Cited by 13 publications

References 22 publications

Method for identifying abnormal power metering data in shared power information collection system

Method for identifying abnormal power metering data in shared power information collection system

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

Evaluation of cost-effective measures for the renovation of existing dwellings in the framework of the energy certification system: A case study in Norway

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