The invention discloses an
electric power consumption probability prediction method based on a neural network, and the method comprises the following steps: collecting historical data of
electric power consumption, dividing the historical data into a
training set and a
test set, and carrying out the normalization
processing of all variables; constructing a neural
network model based on a convolutional architecture and a self-attention mechanism; training a neural
network model by using the processed
training set data, and selecting a model with the best prediction precision as a trained neural
network model by using the
test set; recent data of
power consumption are selected and preprocessed, the preprocessed recent data are input into the model, and an output value of the model is subjected to inverse normalization
processing to obtain a probability prediction result. Compared with a traditional
power load prediction method, the method has the advantages that modeling of
power consumption data of different users in a
power grid is achieved at the same time by means of the constructed neural network model, short-term and long-term
modes in a
time sequence can be captured, high-precision prediction of the
time sequence is achieved, and a point prediction result and a probability prediction result are output.