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Summary
This summary is machine-generated.

This study introduces novel entropy-based metrics derived from smart meter data to detect human activity in homes. These methods offer a cost-effective alternative to dedicated sensors for energy management and automation.

Keywords:
energy demandentropy applicationsprivacy

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Smart meters offer detailed energy data valuable for consumers and providers.
  • Detecting human activity typically requires dedicated sensors, increasing costs.
  • Existing methods for occupancy detection from smart meter data have limitations.

Purpose of the Study:

  • To propose and evaluate new entropy-based metrics for inferring human activity from smart meter readings.
  • To explore the application and parameter effects of these novel metrics.
  • To establish a foundation for future research in smart meter-based activity recognition.

Main Methods:

  • Developed two new metrics: sliding window entropy and interval entropy, inspired by Shannon's entropy.
  • Applied these metrics to the publicly available ECO dataset for analysis.
  • Compared performance against established methods like Page-Hinkley test and geometric moving average.

Main Results:

  • The proposed entropy-based metrics demonstrate competitive accuracy and area under the curve compared to existing statistical methods.
  • Experimental results validate the practical relevance of using smart meter data for activity detection.
  • The approach effectively utilizes energy consumption patterns to infer human presence.

Conclusions:

  • Novel entropy metrics derived from smart meter data can effectively detect human activity without additional sensors.
  • This approach provides a viable and practical solution for applications like home automation and demand-side management.
  • The findings highlight the potential of smart meter data for advanced residential monitoring.