The ‘SmartNIALMeter’ electrical appliance disaggregation dataset

TL;DR

The SmartNIALMeter dataset provides detailed electrical appliance usage data from 20 buildings to improve energy monitoring and grid management.

Contribution

A novel dataset with diverse electrical appliances and sub-metered data collected over two years for advancing NILM research.

Findings

The dataset includes 100 appliances across 20 buildings with five-second sampling intervals.

It features single-phase and three-phase devices, heat pumps, and EV charging stations for varied analysis.

The data supports developing NILM algorithms that require minimal sub-metered information.

Abstract

Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project “SmartNIALMeter” brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance's consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data.

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