The application relates to a
power load prediction method based on a CRKformer model,
electronic equipment and a storage medium, which comprises the following steps: acquiring
time series data, constructing a
data set and performing normalization preprocessing; converting the standardized data to a
frequency domain, obtaining embedded features through linear dense operation, performing context shaping filter calculation, and obtaining a
time domain optimization feature sequence through inverse
Fourier transform; obtaining
time series dependent features through the combination of self-attention mechanism and cross-attention mechanism of endogenous variable sequence embedding in the strong position coding weight; fitting a nonlinear relationship through a Kolmogorov-Arnold network, and enhancing the
time series dependent features by using a learnable
activation function; generating a prediction result by linearly projecting the endogenous output embedding, performing inverse normalization, and outputting a load prediction value. Compared with the prior art, the application has the advantages of inhibiting
noise interference, accurately capturing time series dependence, and high prediction accuracy.