Load rate analysis and evaluation method and system based on multi-source data fusion and deep learning

The invention discloses a load rate analysis and evaluation method based on multi-source data fusion and deep learning, and the method mainly comprises the steps: carrying out the preprocessing of data, collecting the actual load rate and rated load rate of a power system, carrying out the preprocessing of the data, fusing a plurality of collected data sources, obtaining a data set containing more information, and carrying out the analysis and evaluation of the load rate. The method comprises the following steps: training a data set by using a DBN model, establishing a power system load rate prediction model, verifying the model by using a verification set, predicting new data by using the trained model, predicting the load rate of the power system, analyzing the actual load rate of the power system according to a prediction result and external factor data, and updating the model in time according to an analysis result. And the prediction precision and adaptability are improved. By analyzing and evaluating th