A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting

Danlin Li; Yuchen Zhang; Rui Zhang; Ming Yang; Yu Li; Chuanqi Wang; Yateendra Mishra

doi:10.1049/ein2.70017

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A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting

更新时间：2026-04-19

- A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting
- A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting
- Energy Internet 2026年3卷第1期
- 作者机构：
  
  1. School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, New South Wales,Australia
  2. Contribution: Conceptualization, Methodology, Data curation, Investigation, Validation, Formal analysis, Visualization, Writing - original draft
  3. School of Electrical Engineering and Robotics, Queensland University of Technology,Queensland,Brisbane,Australia
  4. Contribution: Data curation, Supervision, Funding acquisition, Project administration, Resources, Writing - review & editing, Conceptualization
  5. Contribution: Software, Supervision, Funding acquisition, Project administration, Writing - review & editing, Conceptualization
  6. Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University,Jinan,China
  7. Contribution: Resources, Data curation, Investigation, Writing - review & editing
  8. Department of Electrical Engineering, City University of Hong Kong,Hong Kong,China
  9. Contribution: Resources, Investigation, Writing - review & editing
  10. Contribution: Resources, Validation, Writing - review & editing
  11. Contribution: Project administration, Investigation, Writing - review & editing
- 作者简介：
- 基金信息：
- DOI：10.1049/ein2.70017
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Danlin Li, Yuchen Zhang, Rui Zhang, 等. A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting[J]. Energy Internet, 2026,3(1).

Danlin Li, Yuchen Zhang, Rui Zhang, et al. A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting[J]. Energy Internet, 2026, 3(1).
Danlin Li, Yuchen Zhang, Rui Zhang, 等. A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting[J]. Energy Internet, 2026,3(1). DOI： 10.1049/ein2.70017.

Danlin Li, Yuchen Zhang, Rui Zhang, et al. A Data-Driven Multidimensional Profiling Framework for Residential Electricity Users With Application to Load Forecasting[J]. Energy Internet, 2026, 3(1). DOI： 10.1049/ein2.70017.

摘要

Abstract

With the advancement of smart grid and Internet of Things

alongside broad adoption of distributed energy resources

precise profiling of residential users has become vital to grid operational efficiency and load forecasting accuracy. However

existing profiling approaches mainly rely on single-source load data and fail to capture the dynamic behavioural heterogeneity of modern prosumers

limiting forecasting performance and demand-side management effectiveness. This article proposes a data-driven multidimensional profiling framework integrating energy configuration attributes

multisource heterogeneous data validated by Analysis of Variance and dynamic consumption patterns identified through two-stage clustering algorithms combining Dynamic Time Warping Path-Aware Weighted K-Shape with Agglomerative Hierarchical Clustering. Robust data preprocessing is ensured via an Improved Quantum-Inspired Genetic Algorithm coupled with Random Forest Regression and Empirical Mode Decomposition. Case studies using an Australian dataset demonstrate that integrating the generated user profile labels into a Transformer-based forecasting model reduces normalised root mean square error by 41.6% compared to baselines. The framework successfully characterises complex user behaviours

providing a scalable methodology for load forecasting

personalised energy services and precise demand response while enabling utilities to better accommodate distributed resources and support renewable energy integration critical to power system stabilisation.

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