Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

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.