A power extraction approach with load state modification for energy disaggregation

• The observed primary deviation in energy disaggregation currently arises from predicting the power of load in the on state. • An approach for extracting the operational power of the target load from aggregated signals is proposed. • The power extraction approach can significantly improve the accuracy of current literature's energy disaggregation methods. • State modification method based on load non-operating time and operating time. Energy disaggregation is a technology that disassembles the energy consumption from the entire house into load-level contributions. One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load. However, current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers, usage times, and operating conditions. In this study, we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios. To be specific, the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods, and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence. Subsequently, the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes. Furthermore, a removing signal spikes method is proposed to improve the accuracy of the extracted power value. We conducted extensive experiments on a public dataset, demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution. The average of mean absolute error across commonly used appliances during on state were reduced by 44.75 % and 32.07 % respectively in the UK-DALE and REFIT datasets.