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Data-driven load profiles and the dynamics of residential electricity consumption
Mehrnaz Anvari1, Elisavet Proedrou2, Benjamin Schäfer3,4,5
1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412, Potsdam, Germany. anvari@pik-potsdam.de.
Nature Communications
|August 6, 2022
Summary
Understanding household electricity consumption fluctuations is key for sustainable energy. This study introduces a data-driven model to separate average demand from intermittent fluctuations, aiding grid planning.
Area of Science:
- Energy Systems Analysis
- Sustainable Energy Grids
- Time Series Data Analysis
Background:
- Planning sustainable energy systems requires understanding power consumption dynamics.
- While renewable energy generation variability is studied, consumption dynamics, especially fluctuations, are less understood.
- Existing load models often lack the granularity to capture intermittent demand variations.
Purpose of the Study:
- To analyze high-resolution residential electricity consumption data from Austria, Germany, and the UK.
- To develop a generally applicable, data-driven load model for disentangling demand profiles and fluctuations.
- To provide tools for understanding and modeling demand dynamics beyond standard load profiles (SLP).
Main Methods:
- Analysis of highly resolved, time-series electricity consumption data from European households.
- Development of a data-driven approach to separate average demand from demand fluctuations.
- Introduction of a stochastic model to quantify intermittent demand fluctuations.
Main Results:
- Successfully disentangled average demand profiles from demand fluctuations using only time series data.
- Developed a generally applicable load model for analyzing residential electricity consumption.
- Quantified the intermittent nature of demand fluctuations with a novel stochastic model.
Conclusions:
- Enhanced understanding of demand dynamics, particularly fluctuations, is crucial for sustainable energy systems.
- The proposed data-driven model and tools can improve the planning and operation of future energy grids.
- Insights support maintaining supply-demand balance in compliant (micro) grids.
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