Power demand prediction system and method based on deep fusion network

By using adaptive weighting and coupled coordination analysis of deep fusion networks, the problem of balancing linear and nonlinear relationships in power demand forecasting is solved, achieving high-precision forecasting with multi-scale feature extraction and time-series dependence, thus improving the accuracy and robustness of power demand forecasting.

CN121616013APending Publication Date: 2026-03-06MARKETING SERVICE CENT OF STATE GRID HENAN ELECTRIC POWER CO
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Patent Information

Application Number
CN202511794598.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-03-06

AI Technical Summary

Technical Problem

Existing power demand forecasting technologies have limitations in feature engineering and model architecture design, failing to effectively balance linear and nonlinear relationships and struggling to capture multi-scale local features and long-term and short-term time series dependencies, resulting in insufficient forecast accuracy and generalization ability.

Method used

A prediction method based on deep fusion networks is adopted. Significant influencing factors are screened through an adaptive weighting mechanism. Combined with the Almon multinomial distributed lag model and coupling coordination analysis, a convolutional neural network and a bidirectional long short-term memory network model are constructed to achieve high-precision prediction of multi-scale feature extraction and time-dependent features.

Benefits of technology

It improves the accuracy and robustness of electricity demand forecasting, enables more accurate identification of complex electricity load characteristics, and enhances the model's generalization ability and forecasting accuracy.

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Abstract

The invention discloses a power demand prediction system and method based on a deep fusion network, and the method comprises the steps: carrying out the standardization preprocessing of multi-source data, introducing a self-adaptive weighting mechanism based on relation complexity, and dynamically fusing a plurality of statistical correlation coefficients to precisely recognize significant influence factors, the technical problem that traditional fixed weight screening is difficult to consider linear and nonlinear relationships is solved. Then, an Almong polynomial distribution lag model and coupling coordination degree analysis are utilized to quantify time lag contribution of influence factors and process feature interaction, and a final feature set including a dynamic conduction mechanism is constructed. And finally, through a deep fusion model integrating the convolutional neural network and the bidirectional long-short-term memory network, multi-scale local features and bidirectional long time sequence dependence are extracted in parallel, so that high-precision prediction of the power demand is realized, and the defects that an existing model is weak in generalization ability and difficult to capture a complex time sequence rule are overcome.
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