Two-stage network supply load prediction method considering optimal parameters

By using the pollination algorithm to optimize the parameters of EEMD and BiGRU in the load sequence prediction in small hydropower areas, and perform error correction, the problem of lack of guidance on parameter settings and large prediction errors in the prior art is solved, and a higher load prediction accuracy is achieved.

CN119674916APending Publication Date: 2025-03-21CHINA THREE GORGES UNIV
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Patent Information

Application Number
CN202411477970.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-22
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

When the prior art deals with the nonlinearity and volatility of load sequences in small hydropower areas, the lack of guidance standards for parameter setting, resulting in unsatisfactory decomposition effect, and the residual components are prone to predict errors and reduce prediction accuracy.

Method used

The flower pollination algorithm (FPA) is used to solve the decomposition parameters of integrated empirical modal decomposition (EEMD), optimize the hyperparameters of the bidirectional gating recursive unit (BiGRU), establish a prediction model of each modal component, and reduce the inherent error of the model through the error correction model.

Benefits of technology

By optimizing parameters and error correction, the load prediction accuracy is significantly improved, and the impact of model inherent errors and short-term factors on the prediction results are reduced.

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Abstract

The invention discloses a two-stage network supply load prediction method considering optimal parameters, and the method comprises the steps: defining an integrated empirical mode decomposition (EEMD) evaluation index suitable for load prediction, and solving a decomposition parameter optimal solution under the minimum evaluation index through employing a flower pollination algorithm FPA; performing hyper-parameter optimization on the BiGRU by using a flower pollination algorithm (FPA), and establishing a prediction model of each modal component to obtain a preliminary prediction result of each modal component; and superposing the preliminary prediction result and the actual value of each modal component to perform subtraction to obtain an error sequence as an output, combining each modal component with a short-term factor as an input, and establishing a mapping relation between the error sequence and the input, thereby performing error correction to obtain a final load prediction result. According to the two-stage network supply load prediction method considering the optimal parameters, high-precision load prediction can be carried out on a load sequence with strong nonlinearity and high volatility.
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