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The Empty-Nest Power User Management Based on Data Mining Technology.

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  • 1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

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Summary
This summary is machine-generated.

Data mining effectively identifies empty-nest households and analyzes their electricity usage. This approach improves abnormal electricity consumption detection, aiding power companies in better serving this growing demographic.

Keywords:
abnormal power consumption detectionanalysis of power consumption behaviordata miningempty-nest user identification

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Area of Science:

  • Data Science
  • Energy Management
  • Sociology

Background:

  • The increasing elderly population leads to a rise in empty-nester households.
  • Effective management of empty-nesters requires advanced technological solutions.
  • Data mining offers potential for personalized energy management strategies.

Purpose of the Study:

  • To develop and validate a data mining-based method for identifying empty-nest power users.
  • To analyze the electricity consumption patterns of empty-nest households.
  • To detect abnormal electricity consumption behavior in empty-nester populations.

Main Methods:

  • Utilized a weighted random forest algorithm for empty-nest user identification.
  • Employed fusion clustering with adaptive cosine K-means for analyzing electricity consumption behavior.
  • Developed an anomaly detection model combining Auto-regressive Integrated Moving Average (ARIMA) and isolated forest algorithms.

Main Results:

  • The weighted random forest algorithm achieved a 74.2% accuracy in identifying empty-nest users.
  • The adaptive cosine K-means algorithm demonstrated superior performance with the shortest running time (3.4281 s) and lowest Sum of Squared Error (SSE) (31.6591).
  • The anomaly detection model successfully recognized abnormal electricity consumption with 86% accuracy.

Conclusions:

  • The proposed data mining methods effectively identify empty-nest users and their consumption patterns.
  • The anomaly detection model accurately identifies abnormal electricity usage among empty-nesters.
  • These findings support improved energy management and service delivery for empty-nester households.