Day-ahead electricity price prediction method based on crisscross algorithm and deep learning model

Abstract

The invention relates to the technical field of electricity price prediction, in particular to a day-ahead electricity price prediction method based on a crisscross algorithm and a deep learning model, and the method comprises the following steps: 1), collecting the original electricity price data of a high-proportion new energy electricity market, and carrying out the preprocessing of the original electricity price data; 2) establishing an LSTM prediction model, and taking the day-ahead electricity price, load, wind power and photovoltaic generating capacity before a prediction day as feature input of the LSTM prediction model; 3) adopting a conventional gradient descent method to train the LSTM prediction model for the first time; and 4) taking the minimum mean square error as an objective function, carrying out fine adjustment on the weight coefficient and bias between the fully connected layers based on a crisscross algorithm, and obtaining a final optimized long and short term memory network deep learning model. The method can effectively prevent the weight coefficient and bias of the deep learning model from falling into local optimum, improves the generalization performance of the model, and improves the prediction precision of the day-ahead electricity price.

Description

technical field [0001] The present invention relates to the technical field of electricity price forecasting, and more specifically, to a day-ahead electricity price forecasting method based on a vertical and horizontal intersection algorithm and a deep learning model. Background technique [0002] In recent years, vigorously developing renewable energy such as wind energy and photovoltaics has become an important measure to deal with resource depletion and environmental pollution, and large-scale new energy is gradually being incorporated into the grid. Due to the low marginal cost of new energy sources such as wind and solar, the day-ahead clearing price of the electricity market has shown a downward trend; moreover, electric energy cannot be stored in large quantities, and the balance between supply and demand must be met at all times. The price fluctuates strongly and presents non-stationary characteristics. In the context of a power market with a high proportion of new ...

AI Technical Summary

Problems solved by technology

The above scheme uses Bi-LSTM to predict the price of electricity, and can mine the regularity of features in time series, but there is a defect that the weight coefficient and bias are easy to fall into the local optimum, which leads to the weak generalization ability of the model

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