Short-term power load prediction method based on CNN-IPSO-GRU hybrid model

The invention discloses a short-term power load prediction method based on a CNN-IPSO-GRU hybrid model. The method comprises the following steps: firstly, collecting historical load, meteorological factors, date information and other data of a power grid, performing data normalization processing, dividing a training set and a test set, extracting a multi-dimensional feature vector representing load change by using a convolutional neural network technology, and constructing a time sequence as input of a model; constructing a gating circulation unit network prediction model, optimizing the gating circulation unit network prediction model by utilizing the training set data through an improved particle swarm algorithm to obtain two optimal prediction model parameters, and reestablishing a gating circulation unit network model according to the obtained optimal prediction model parameters; and finally, realizing the short-term load prediction of the power grid by test set data. The method provided by the invention