Aiming at the problem of weak internal regularity caused by the characteristics of nonlinear and strong fluctuation of load data

a TCN-WOA-BiLSTM-Attention power load short-term prediction model based on Bootstrap error correction was constructed. Temporal convolutional network(TCN) was used to extract temporal features and the contribution of important information to the features was highlighted through the Attention mechanism. The whale optimization algorithm(WOA) was employed to find the optimal bidirectional long short term memory network(BiLSTM) hyperparameters

thus to reduce the negative impact of manual search hyperparameters and then forecast. Based on Bootstrap analysis on error distribution of the prediction interval

the necessity of correcting the prediction result was judged by whether the PICP was lower than the corresponding confidence

and the reasonable correction range was selected. The results show that

the error correction based on the Bootstrap method can avoid the problem of insufficient correction and excessive correction. Compared with the method of correcting the whole error sequence

it is more scientific and improves the prediction accuracy of the model to the greatest extent.