4.6 Review

A Comprehensive Review of Residential Electricity Load Profile Models

Journal

IEEE ACCESS
Volume 9, Issue -, Pages 12114-12133

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3050074

Keywords

Load modeling; Biological system modeling; Demand side management; Solid modeling; Predictive models; Power demand; Faces; Load modeling; demand forecasting; load management; smart homes; power demand; electricity consumption; residential electricity load profile model; demand-side management; household electricity load profile model; residential power demand

Funding

  1. German Federal Ministry for Economic Affairs and Energy (Bundesministerium fur Wirtschaft und Energie) (BMWi)

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One third of the final electricity in the EU is consumed by households, with changes in usage patterns driven by increased electrical device usage, electromobility, and work-from-home arrangements. This research evaluates current residential electricity load profile modeling, proposes future research directions and solutions to challenges faced by modelers, such as privacy concerns. Solutions include anonymization algorithms, crowd sourcing, and increased funding for privacy-proof smart meters.
A third of the final electricity in the EU is consumed by households. The increased usage of multiple electrical devices, electromobility, self-generation and consumption of electricity as well as work-from-home will fundamentally change the residential electricity load profile, so a deep understanding of the current state of residential electricity load profile modelling is necessary. The objective of this paper is to perform a literature review, evaluate the current state of the residential electricity load profile modelling, categorise the models, propose future research directions and applications, identify the challenges the researchers face when building these models and offer possible solutions. Thirty two residential electricity load profile models are identified and a new definition of the residential electricity load profile model is proposed. A new categorisation system based on the identification of the main features of these thirty two studies is introduced. Future research directions and applications are presented and the most important challenges that modellers face when attempting to build such models are identified and discussed. The most important challenge identified is the privacy concerns of the participants or potential participants. These concerns are at least partially responsible for the existence of the rest of the challenges. The creation and implementation of an anonymisation algorithm, before any human has access to any measured datasets, the implementation of a crowd sourcing approach which addresses the privacy concerns of the citizens and increased funding for the installation of privacy-proof smart-meters by the public and measurement campaigns are identified as possible solutions to the challenges faced by modellers.

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