Nonintrusive load monitoring (NILM) deconstructs aggregated electrical usage data into individual appliances. The dissemination of disaggregated data to customers raises consumer awareness and encourages them to save power, lowering CO2 emissions to the environment. The performance of NILM systems has increased dramatically thanks to recent disaggregation methods. However, the capacity of these algorithms to generalize to various dwellings as well as the disaggregation of multi-state appliances remain significant obstacles. In this paper, we propose an energy disaggregation approach by using socio-economic parameters. The suggested approach helps in creating more accurate load profiles, which improves the accuracy and helps in better detection of the appliances. The proposed model outperforms state-of-the-art NILM techniques on the PRECON dataset. The mean absolute error reduces by 5% – 10% on average across all appliances compared to the state-of-the-art. Thus, improving the detection of the target appliance in the aggregate measurement.
Keywords NILM, LSTM, advanced energy technologies, energy conservation in buildings