Power load prediction method and device, equipment and storage medium

CN122026311APending Publication Date: 2026-05-12CHINA IND INTERNET RES INST
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Patent Information

Application Number
CN202512013144.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-29
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing technologies for forecasting power load in industrial parks suffer from problems such as mode aliasing leading to decomposition failure, lack of causality causing interpretation failure, and abnormal historical interference leading to long-term forecast instability, which affect dispatch reliability and increase energy costs.

Method used

An integrated model for power load forecasting, employing variational mode decomposition and causal inference, is used to split the power load of an industrial park into multiple stationary mode components. Effective features are then selected through causal graphs and Granger causality tests to construct targeted prediction sub-models, ultimately forming an integrated model for forecasting.

Benefits of technology

It enables accurate prediction of power load in industrial parks, reduces energy costs, improves the accuracy and stability of predictions, and provides reliable data support for power dispatch.

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Abstract

The invention discloses a power load prediction method and device, equipment and a storage medium, and relates to the technical field of power load prediction, and the method comprises the steps: obtaining a to-be-predicted power load request set by a target user for a target location; inputting the to-be-predicted power load request into a power load prediction integrated model, and obtaining a load prediction result output by the power load prediction integrated model, the power load prediction integrated model is obtained by performing variational mode decomposition and fusion causal inference on the basis of historical power load data, production operation data and associated influence data of the target location. According to the method, different types of loads are split through variational mode decomposition, the pertinence and accuracy of prediction are improved, meanwhile, effective features are screened through fusion of causal inference, redundant information interference is reduced, the model performance is optimized, accurate prediction of the power load of the industrial park is achieved, reliable data support is provided for power dispatching, and the energy cost is reduced.
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