Electricity price prediction method, device, equipment, storage medium and program product

By integrating power grid data to form a time-series dataset and utilizing feature extraction and prediction models, the problems of data feature loss and period limitations in existing electricity price prediction methods are solved, achieving high-precision electricity price prediction and long-term adaptability, and optimizing the scheduling of energy storage systems.

CN122434586APending Publication Date: 2026-07-21BEIJING HYPERSTRONG TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING HYPERSTRONG TECH CO LTD
Filing Date
2026-04-14
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing electricity price forecasting methods rely on multi-stage information distillation, which leads to the loss of original data features. This makes it difficult to meet the high-precision requirements of energy storage power stations for accurate peak and valley identification, and it cannot be dynamically extended to longer periods for forecasting, resulting in energy storage system scheduling decisions being limited to local optima.

Method used

By acquiring power grid data and integrating it into a time-series dataset, and utilizing time-series features and electricity price prediction models, predictions are made based on the dependency relationship between historical and current electricity prices. Convolutional networks and long short-term memory neural network models are used to extract feature correlation vectors, and sliding window technology is combined to dynamically adjust the data length, thereby improving the accuracy of electricity price prediction and the ability to predict long-term periods.

Benefits of technology

It improves the accuracy and adaptability of electricity price forecasting, can dynamically extend the forecast period, meet the high-precision peak-valley identification requirements of energy storage power stations, and optimize the scheduling decisions of energy storage systems.

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Abstract

Embodiments of the present application provide a power price prediction method, device, equipment, storage medium and program product. The power price prediction method comprises the following steps: obtaining power grid transaction data, a first power price corresponding to N time points in a first time period, and a second power price corresponding to a second time period; performing data integration processing on the power grid transaction data, the first power price, and the second power price according to the N time points to obtain a time series data set, wherein the time series data set comprises the power grid transaction data, the first power price, and the second power price corresponding to each time point; and predicting the power price corresponding to the N time points in the second time period based on the time series data set and a power price prediction model to obtain a predicted power price corresponding to each time point in the second time period. The method integrates power grid data according to time sequence characteristics to obtain a time series data set, and improves the accuracy of power price prediction and the prediction ability of long-period power prices based on the dependence relationship between historical power prices and current power prices in the time series data set.
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