Abstract: The present disclosure relates to non-intrusive load decomposition techniques, and aims at providing a non-intrusive load decomposition method based on an Informer model coding structure. The method includes: preprocessing power data in electricity loads, and forming sample pairs according to a time sequence of total power and a time power sequence of a single electricity load; building a training model with reference to an Informer model, the training model including a feature extraction part, a feature processing part, and a feature mapping part which are arranged in sequence; initializing parameters of the training model, and selecting an appropriate activation function and loss function; training the training model using preprocessed sample data; and inputting a total power curve to the trained model, and conducting decomposition to obtain a power curve of a single load.

Abstract: The present disclosure relates to non-intrusive load decomposition techniques, and aims at providing a non-intrusive load decomposition method based on an Informer model coding structure. The method includes: preprocessing power data in electricity loads, and forming sample pairs according to a time sequence of total power and a time power sequence of a single electricity load; building a training model with reference to an Informer model, the training model including a feature extraction part, a feature processing part, and a feature mapping part which are arranged in sequence; initializing parameters of the training model, and selecting an appropriate activation function and loss function; training the training model using preprocessed sample data; and inputting a total power curve to the trained model, and conducting decomposition to obtain a power curve of a single load.