The invention discloses a day-ahead
electricity price probability prediction method and device based on
deep learning, and the method comprises the steps: obtaining the meteorological state data of a to-be-predicted region and the power generation data of a power generator, and constructing prediction parameters; inputting the prediction parameter into a pre-trained day-ahead
electricity price probability prediction model for prediction to obtain a day-ahead
electricity price probability; the training method comprises the following steps: collecting meteorological state data of a region and power generation data of a generator, and constructing a historical
data set; performing visual analysis and data preprocessing on the historical
data set to obtain a prediction
feature parameter set; and taking the prediction characteristic parameter set as input, taking a deep neural network (DDNN) as a basic model for training, adding hyper-parameter optimization in the training process, and carrying out probability prediction on the day-ahead
electricity price by using an output probability distribution parameter. According to the method, the possible fluctuation range of the predicted value can be provided, the uncertainty level of the predicted result is reflected, and information and basis are provided for
decision making such as planning prediction and operation of a power
system.