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Received: 22 December 2019 Accepted: 18 March 2020 Drawing on the principles of non-intrusive load monitoring (NILM) and the load parameter detection function of smart remote load controller (SRLC), this paper presents a non-intrusive load identification method based on real-time extraction of multiple steady-state parameters and the optimization of state coding. Firstly, the characteristic parameters of loads were extracted by the Intelligent Power Management Platform, and the original load data were clustered by the improved affinity propagation (AP) algorithm, creating a sample set of multiple steady-state parameters. Considering the working states of loads, a load decomposition model was established, and the objective function was optimized by genetic algorithm (GA), realizing the decomposition and re-identification of household loads. Finally, our method was proved to have an accuracy of over 96% through experiments. The research results provide a reference for electricity department to identify the type and features of loads on the consumer side.

Non-invasive Load Identification Based on Real-Time Extraction of Multiple Steady-State Parameters and Optimization of State Coding