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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Electricity forecasting on the individual household level enhanced based on activity patterns.

Krzysztof Gajowniczek1, Tomasz Ząbkowski1

  • 1Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland.

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This study enhances household electricity load forecasting by incorporating resident behavior patterns. Accurate 24-hour predictions improve energy efficiency in smart homes.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Energy Systems

Background:

  • Smart metering enables energy efficiency but requires accurate load forecasting.
  • Individual household load forecasting presents unique challenges compared to aggregate data.
  • Predicting electricity usage is crucial for intelligent smart meter functionality.

Purpose of the Study:

  • To propose an enhanced approach for short-term electricity load forecasting at the individual household level.
  • To improve forecasting accuracy by integrating resident daily activities and appliance usage patterns.
  • To develop Residential Power Load Forecasting (RPLF) methods tailored for individual homes.

Main Methods:

  • Utilized a household-specific dataset capturing behaviors influencing power consumption.
  • Employed segmentation and sequence mining algorithms to derive behavioral patterns.
  • Conducted an extensive study comparing various forecasting algorithms enhanced with activity patterns.

Main Results:

  • Demonstrated improved accuracy in 24-hour ahead electricity load forecasting for individual households.
  • Validated the effectiveness of incorporating household activity patterns into forecasting models.
  • Provided a robust methodology for Residential Power Load Forecasting (RPLF).

Conclusions:

  • Integrating resident behavior significantly enhances household electricity load forecasting accuracy.
  • The proposed methods offer a valuable tool for smart meter intelligence and energy management.
  • This research advances the field of individual-level power load prediction.