Electricity price prediction method and relevant device

Abstract

The invention discloses an electricity price prediction method. Through the method, historical electricity price data can be decomposed through a mode decomposition algorithm to obtain multiple discrete modes, then an optimized prediction model is utilized to perform prediction on each discrete mode, a prediction result corresponding to each discrete mode can be obtained, and a prediction result of electricity price can be obtained just by superposing all the prediction results. Since mode decomposition is performed on highly-nonlinear historical electricity price data and then mode decomposition results are predicted, for each decomposition result, the problem of high nonlinearity can be avoided in the prediction process, and consequently the prediction result under each discrete mode canbe more accurate; and the actual prediction result based on the historical electricity price data can be obtained just by superposing all the prediction results, and therefore the actual prediction result can be more accurate. The invention furthermore provides an electricity price prediction system and device and a computer readable storage medium, all of which also have the advantages.

Description

technical field [0001] The present invention relates to the field of electricity price prediction, and more specifically, to an electricity price prediction method, system, device and computer-readable storage medium. Background technique [0002] With a series of reforms in my country's electricity market, the degree of marketization of the electricity market has gradually increased, and the degree of monopoly has gradually decreased. Under marketization conditions, certain methods can also be used to predict electricity prices, thereby optimizing market resources, promoting the process of marketization, maximizing the interests of market participants, and making the development of the electricity market more stable, orderly, and healthy. With the continuous deepening of electricity marketization, the importance of electricity price forecasting has become more and more prominent. A reasonable electricity price forecasting mechanism is related to the vital interests of marke...

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Problems solved by technology

[0003] At present, extreme learning machines can be applied to electricity price forecasting. However, traditional single extreme learning machine forecasting models are often difficult to deal with the impact of highly nonlinear electricity prices on forecasting results.

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