Power price prediction method based on improved deep belief network

Abstract

The invention discloses a power price prediction method based on an improved deep belief network, and the method comprises the steps: dividing a data set and determining the input of network data according to the characteristics of electricity price data and the influence factors of electricity price, and carrying out the data preprocessing of an adopted data set; for the preprocessed data set, calculating a network error by using a second-order reconstruction error, and determining the number of layers of the model RBM; optimizing the number of neuron nodes in the network by using a '3 + 2 'search algorithm combining a trisection method and a bisection method; using a BP neural network and an SVR support vector regression machine used as regression layers of a DBN network, and using the number of layers of an RBM and the number of optimized neuron nodes to construct a DBN-BP model with an optimized structure and the DBN-SVR model with an optimized structure; and predicting the real-time electricity price data. According to the invention, the DBN model with an optimized structure is established, and different combination improvements are carried out on the regression layer of the network, so that the prediction precision of the DBN is improved, and the application prospect is very good.

Description

technical field [0001] The invention relates to a power price prediction technology, in particular to a power price prediction method based on an improved deep belief network. Background technique [0002] With the development of the energy Internet and the gradual deepening of my country's power reform, a large number of small and medium-sized power companies and power users have poured into the market, eager to obtain faster and more accurate power market information from the Internet and provide more convenient services. In a competitive electricity market environment, electricity is traded as a commodity, and electricity price is the general term for the price of electricity per unit of electricity. In the process of power trading in the market, trading participants include power generators, power sellers, and power buyers. They pay more attention to the determination of power prices. The flow direction and rationing in the electricity market can reflect the supply and ...

AI Technical Summary

Problems solved by technology

However, due to the structural characteristics of the deep belief network, different neural layers and the number of neural network nodes will have an impact on the model results. Therefore, how to determine the appropriate number of neural layers and neuron nodes has become a technical difficulty.

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