Kernel density estimation-based non-invasive power load identification method

The invention relates to a kernel density estimation-based non-invasive power load identification method. The method comprises the following steps of: selecting a common household power load as a research object, acquiring power consumption data of the research object, carrying out sub-state division and extraction power distribution; generating a household working state set according to the power distribution, and calculating simulation power consumption data under each state; carrying out kernel density estimation to obtain probability distribution reference model of each state simulation data; identifying household working state transition points in the reference models, and dividing each household working state data segments; and for each data segment, searching a household working state which is closest to the probability distribution of the data segment, and comparing the household working state with the probability distribution so as to complete an identification task. According to the method, the main