Electricity price prediction method based on quantum immune optimization BP neural network algorithm

Abstract

The invention provides an electricity price prediction method based on a quantum immune optimization BP neural network algorithm, and belongs to the technical field of electricity price prediction, and the method comprises the steps: inputting the index values of a plurality of groups of electricity price impact factors into an electricity price prediction model, wherein each group of electricityprice influence factors comprises a plurality of electricity price influence factors, the electricity price prediction model is obtained by training a BP neural network which is globally optimized byusing a quantum immune optimization algorithm by using multiple groups of training data, and each group of training data comprises an index value of a group of electricity price influence factors andan electricity price change state corresponding to the index value of the group of electricity price influence factors; and obtaining output information of the electricity price prediction model, wherein the output information comprises an electricity price change state type corresponding to the index value of the electricity price influence factor. According to the method, modeling of electricityprice prediction is completed by adopting a quantum immune optimization BP neural network algorithm, the nonlinear mapping capability is high, the network architecture is flexible, the calculation convergence speed is high, the electricity price prediction precision is improved, maintenance manpower and financial resources are saved, and the prediction period is shortened.

Description

technical field [0001] The invention relates to the technical field of electricity price prediction, in particular to an electricity price prediction method based on quantum immune optimization BP neural network algorithm. Background technique [0002] As a special commodity, electricity price prediction is the core of the whole market, and the fluctuation of electricity price affects the flow and allocation of resources in the electricity market. The determination of electricity price should conform to the value law of electric energy production and consumption. Electricity price has the characteristics of obvious periodic changes. During the peak load stage, the electricity price fluctuates greatly, and it is easy to have a spike in electricity price. The main reason is that during the peak load, there are often problems such as the scheduling of units outside the market and the strategic bidding of participants. These factors are likely to cause peaks in electricity price...

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Problems solved by technology

[0004] The existing electricity price prediction method based on BP neural network algorithm has a strong self-learning ability and can approach any continuous function, but there are problems such as slow learning speed, low precision, and easy to fall into local minimum points.

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