Assessing the Granularity/Resolution of Household Electrical Load Data for Appliance-Level Disaggregation

By Utkarsh Misra

Accurate disaggregation of household electrical load datainto individual appliance-level consumption is crucial for energy efficiency initiatives and retail demand-side management strategies. This research investigates the optimal temporal resolution for disaggregation by analyzing the information content embedded within 15-minute interval load data collected from households in a specific state. We evaluate whether this 15-minute sampling adequately captures the characteristic signatures of common household appliances, or if it suffers from either oversampling, leading to redundant information, or undersampling, causing a loss of critical details.

We compare the performance of disaggregation models trained on 15-minute data against models utilizing downsampled and upsampled versions of the data. By analyzing model accuracy, feature importance, and computational efficiency, we assess whether 15-minute intervals strike a balance between information richness and data volume.

The findings of this research will ignite the conversation regarding the optimal sampling resolution for household load data disaggregation. Our results will inform researchers and practitioners about the suitability of 15-minute data for accurate appliance-level analysis, guiding future data collection efforts and disaggregation algorithm development. Additionally, the study sheds light on the trade-off between temporal resolution, information capture, and computational resources, paving the way for optimal and insightful disaggregation approaches.

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