The invention discloses a day-ahead
electricity price prediction method based on long-term and short-term
time series networks of an attention mechanism. The method comprises the following steps: acquiring a
data set related to
electricity price; calculating the correlation between the
electricity price influence factors and the
electricity price change in the
data set so as to sequentially screen out the
electricity price influence factors with relatively strong correlation, and constructing a sample set for the
data set according to the screened
electricity price influence factors and the electricity price; dividing the sample set into a
training set and a
test set, and normalizing data of the
training set and the
test set; forming an input matrix by a plurality of continuous time sequences in the normalized
training set, inputting the input matrix into long-term and short-term
time sequence network models based on an attention mechanism for training, and finely adjusting parameters in the training process to obtain a day-ahead electricity
price prediction model with optimal parameter configuration as a trained day-ahead electricity
price prediction model; and forming an input matrix by a plurality of continuous time sequences in the
normalized test set, inputting the input matrix into the trained day-ahead electricity price prediction model for testing to obtain a predicted day-ahead electricity price data normalized value, and performing reverse normalization to obtain a day-ahead electricity price prediction value of a standard dimension. According to the invention, the precision of day-ahead electricity price prediction can be effectively improved.