Short period electricity price prediction method, apparatus and system

Abstract

The invention discloses a short period electricity price prediction method, apparatus and system. The method includes acquiring the pre-processed electricity price historical data; adopting a variational mode decomposition method to decompose the electricity price historical data to obtain a plurality of discrete mode components; adopting a pre-established neural network optimization model to predict and process each discrete mode to obtain the predicted value corresponding to each discrete mode component; superposing each predicted value to obtain the electricity price prediction result, wherein the establishment process of the neural network optimization model is to add the training sample data to the neural network; and adopting a crosswise algorithm to optimize the parameters of the neural network to obtain the trained neural network optimization model. In the process of using, the influence of nonstationarity and nonlinearity of the electricity price series on the prediction results is reduced, and the global convergence accuracy and prediction accuracy are improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of power systems, and in particular, to a short-term electricity price prediction method, device and system. Background technique [0002] With the reform of the electricity market, electricity prices can be traded in the market like ordinary commodities. Electricity price forecasting is to fully consider the market supply and demand relationship, market participants' implementation of power market size, and social activities. Conduct research on the data, analyze the changing law of the electricity price itself, and predict the marginal price of the future electricity market. For power generators, accurate electricity price forecasts are conducive to grasping market trends and market opportunities, so as to construct optimal power generation and electricity price bidding strategies to obtain maximum profits; Electricity costs. Accurate electricity price forecasts allow users to contro...

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Problems solved by technology

[0003] At present, in the prior art, a single model is mainly used to predict short-term electricity prices, such as a neural network model, but when a single neural network model is used to predict short-term electricity prices, the parameters of the neural network tend to fall into local optimum, and the prediction results are easily Affected by the non-stationarity and nonlinearity of the electricity price sequence, it affects the prediction accuracy to a certain extent and reduces the prediction accuracy

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