COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
This addresses energy efficiency challenges for households and utilities by improving appliance-level electricity usage analysis, though it is incremental as it builds on existing transformer methods for a known bottleneck.
The paper tackles the problem of disaggregating high-frequency electricity data with multiple simultaneously working devices in non-intrusive load monitoring, proposing COLD, a transformer-based model that achieves 95% load identification accuracy and 82% disaggregation performance on test data while handling up to 11 concurrent loads.
The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus. Non-intrusive load monitoring (NILM) enables detailed analyses of household electricity usage by disaggregating the total power consumption into individual appliance-level data. In this paper, we propose COLD (Concurrent Loads Disaggregator), a transformer-based model specifically designed to address the challenges of disaggregating high-frequency data with multiple simultaneously working devices. COLD supports up to 42 devices and accurately handles scenarios with up to 11 concurrent loads, achieving 95% load identification accuracy and 82% disaggregation performance on the test data. In addition, we introduce a new fully labeled high-frequency NILM dataset for load disaggregation derived from the UK-DALE 16 kHz dataset. Finally, we analyze the decline in NILM model performance as the number of concurrent loads increases.