This paper proposes a new appliance-driven selection of sampling frequencies for improving the energy disaggregation performance in non-intrusive load monitoring. Specifically, the methodology uses a machine learning model with parallel device detectors and optimized device dependent sampling rates in order to improve device identification. The performance of the proposed methodology was evaluated on a state-of-the-art baseline system and a set of publicly available databases increasing performance up to 6.7% in terms of estimation accuracy when compared to the baseline energy disaggregation setup without device dependent sampling rates.
|Name||European Signal Processing Conference|
|Conference||27th European Signal Processing Conference, EUSIPCO 2019|
|Period||2/09/19 → 6/09/19|
- Device Classification
- Energy Disaggregation
- Non-intrusive load monitoring (NILM)