Generative adversarial network-based day-ahead spot clearing electricity price-load data restoration method

The invention provides a day-ahead spot clearing electricity price-load data restoration method based on a generative adversarial network. The method comprises the following steps: establishing a training data set; a K-means clustering algorithm is adopted to perform clustering analysis on the user electrical load data set, and typical user groups are divided; establishing a deep convolutional generative adversarial network DCGAN model; training the DCGAN model by using day-ahead historical clearing electricity price data of a spot market and historical user electricity load data after typical user group division, and reserving a generator structure for each type of typical user group; a binary mask matrix is introduced to represent the missing position of newly-collected user electrical load data, then a spot market clearing electricity price data set before a newly-collected day, a typical user electrical load data set and target user electrical load data with missing phenomena are input into a generator co