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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
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Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain.

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  • 1Center for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaén, 23071 Jaén, Spain.

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Summary

Smart meter data resolution significantly impacts household load profile analysis. High-resolution data captures fluctuations, but accuracy varies by household, with 5-second intervals showing promising results.

Keywords:
advanced metering infrastructureelectric load profilesmart metertemporal data granularitytime seriestime slices

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Area of Science:

  • Energy systems analysis
  • Data science and analytics
  • Residential energy consumption

Background:

  • Smart meter (SM) deployment generates high-granularity residential energy data.
  • Temporal resolution choice critically affects load profile feature analysis.
  • Understanding data granularity's impact is vital for accurate energy assessments.

Purpose of the Study:

  • To present a methodology for analyzing household load profile features using various data resolutions.
  • To introduce advanced statistical analyses for enhanced feature description.
  • To propose a framework for high-frequency data collection in households.

Main Methods:

  • Utilized periodograms, autocorrelation, and partial autocorrelation analyses.
  • Applied empirical distribution-based statistical analysis for feature description.
  • Developed a framework for high-sampling-frequency household data collection.
  • Investigated the influence of data granularity on load profile features.

Main Results:

  • High-resolution data is recommended for capturing complete consumption load fluctuations.
  • The accuracy of feature description is load profile dependent; some households benefit from coarse-grained data.
  • An intermediate 5-second data resolution demonstrated feature characterization close to 0.5-second data.

Conclusions:

  • Data granularity significantly influences the description of household consumption profile features.
  • The proposed methodology effectively analyzes the impact of data resolution.
  • Tailoring data resolution to specific household load profiles can optimize analysis accuracy.