Electricity price prediction method and device for electric power spot market

Abstract

The embodiment of the invention provides an electricity price prediction method and device for an electric power spot market, terminal equipment and a storage medium, and the method comprises the steps: obtaining historical electricity price data in a certain time range, and forming a historical electricity price data sequence; normalizing the historical electricity price data sequence, and carrying out wavelet decomposition on the normalized historical electricity price data sequence by adopting a set scale to decompose the normalized historical electricity price data sequence into sub-sequences with different frequencies; dividing the sub-sequences with different frequencies into a training set and a prediction set; training a CNN model by using the training set, and transmitting the prediction set to the trained CNN model for electricity price prediction to obtain prediction data; carrying out wavelet reconstruction and reverse normalization processing on the prediction data, and outputting an actual electricity price prediction sequence. The embodiment of the invention can improve the precision of electricity price prediction in the electric power spot market.

Description

technical field [0001] The present invention relates to the field of electric power technology, in particular to a power price prediction method, device, terminal equipment and storage medium in the power spot market. Background technique [0002] The power spot market can fully reflect the marginal cost of power generation and power supply and demand, thus better highlighting the power price signal. However, the existing electricity price forecasting methods mainly include single or combination algorithms such as regression model, neural network forecasting, time series, wavelet analysis forecasting and gray forecasting, which have poor price forecasting accuracy and algorithm stability when used in the power spot market. [0003] The southern (starting in Guangdong) electricity spot market is about to enter trial operation. This is the first electricity spot market in the country to be put into trial operation. There is an urgent need to propose a price forecasting method ...

AI Technical Summary

Problems solved by technology

However, the existing electricity price forecasting methods mainly include single or combination algorithms such as regression model, neural network forecasting, time series, wavelet analysis forecasting and gray forecasting. The price forecasting accuracy and algorithm stability of the electricity spot market are poor.

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