Daily average power load prediction method based on BP neural network

Abstract

The invention provides a daily average power load prediction method based on a BP neural network. The method comprises the following steps of obtaining numerical values corresponding to a plurality ofmain meteorological factors of a prediction day and a prediction day date; obtaining historical day data used for load prediction model training according to the main meteorological factors and datesof the prediction days; inputting the historical daily data for training into a BP neural network, and optimizing the BP neural network to obtain an optimized BP neural network model; and inputting the main meteorological factors of the prediction day and the prediction day date into the optimized BP neural network model, and calculating to obtain the power load of the prediction day. According to the method, the similar daily algorithm is used for obtaining training data, training of the network is accelerated on the premise that the prediction precision is guaranteed, meanwhile, the weightof the BP neural network is optimized through the genetic algorithm, the problems that the BP neural network is trapped in a local minimum value in random initialization, convergence is difficult andthe like are solved, and the prediction precision of the model is improved.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a method for forecasting daily average power load based on BP neural network. Background technique [0002] BP neural network is a multi-layer feed-forward neural network trained according to the error back propagation algorithm, and it is the most widely used neural network at present. The BP neural network adopts the method of supervised learning and error backpropagation for learning. Its structure has one or more hidden layers, including input layer, hidden layer and output layer. Its main characteristics are: the signal is propagated forward, and the error is propagated backward. Specifically, for a neural network model with only one hidden layer: the process of BP neural network is mainly divided into two stages, the first stage is the forward propagation of the signal, from the input layer through the hidden layer, and finally reaches the output layer; The...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a daily average power load forecasting method based on BP neural network to solve the influence of factors such as weather, date type and time distance on load forecasting in the prior art, and BP neural network The network has the disadvantages of slow convergence and easy to fall into local minimum

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