Method and system for electricity price prediction, and computer readable storage medium

Abstract

The invention provides a method and a system for electricity price prediction, and a computer readable storage medium. The method comprises the following steps: enabling historical electricity price data to be clustered into m types through a K-means algorithm, and obtaining historical electricity price data with a mode label; training and constructing an electricity price mode recognition model according to the historical electricity price data with the mode label; establishing n mutually independent day-ahead electricity price prediction models; respectively obtaining n electricity price prediction sequences of the prediction day through the n mutually independent day-ahead electricity price prediction models; inputting the n electricity price prediction sequences into the electricity price mode identification model, and outputting prediction results of n electricity price daily fluctuation modes; calculating the final voting score of each electricity price daily fluctuation mode according to the prediction results of the n electricity price daily fluctuation modes, and selecting the electricity price daily fluctuation mode with the highest score as the finally predicted electricity price daily fluctuation mode. The method can improve the prediction precision of the daily fluctuation mode of the electricity price, and further improves the prediction accuracy of the day-aheadelectricity price.

Description

technical field [0001] The present invention relates to the electric power field, in particular to an electricity price prediction method, system and computer-readable storage medium. Background technique [0002] Electricity price forecasting is of great significance for formulating a reasonable bidding strategy, maintaining the security and stability of the electricity market, and improving the economy of system operation. The day-ahead electricity price forecast is an important part of the electricity price forecast in the electricity market, and it is mainly used to predict the electricity price trend in the next 24 hours. With the continuous advancement of my country's electricity market reform. Higher requirements are put forward for the accuracy of electricity price forecasting. Therefore, the day-ahead electricity price prediction has become the focus of research in the field of electricity. [0003] In recent years, different theories and methods have been propos...

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Problems solved by technology

Although the above electricity price forecasting methods have greatly improved the original model through data preprocessing or parameter optimization, and have carried out simulation verification in their respective scenarios or electricity markets, the unified modeling based on all historical data The method cannot distinguish the difference in the daily fluctuation pattern of the electricity price series, which leads to the mutual influence of the electricity price data of different daily fluctuation patterns in the process of establishing the forecast model, the fitting degree of the model to the input and output becomes poor, and it is difficult to obtain a more ideal prediction result

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