TCN-GRU-Adaboost-based load prediction method
By adopting the load prediction method based on TCN-GRU-Adaboost in an integrated energy system, the problems of low load prediction accuracy, slow response speed and poor stability in the prior art are solved, and higher prediction accuracy and robustness are achieved.
CN119692520APending Publication Date: 2025-03-25SOUTHWEST PETROLEUM UNIV
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Patent Information
- Application Number
- CN202411573198.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-06
- Publication Date
- 2025-03-25
AI Technical Summary
Technical Problem
The existing load prediction methods have low prediction accuracy, slow response speed and poor stability in integrated energy systems, making it difficult to effectively deal with the coupling relationship and uncertainty of multiple energy needs.
Method used
The load prediction method based on TCN-GRU-Adaboost is adopted to form a hybrid neural network through a time-sequence convolution network, a gated loop unit and an attention mechanism, and the prediction capability of the model is enhanced by an adaptive enhancement algorithm.
Benefits of technology
It improves the accuracy and robustness of load prediction, enhances the model's ability to predict different types of loads, and improves the response speed and stability of the prediction.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides a TCN-GRU-Adaboost-based load prediction method, which is applied to the field of load prediction of an integrated energy system. Firstly, a time sequence signal is simply decomposed and reconstructed; secondly, in combination with a time sequence convolutional network (TCN), a gating cycle unit (GRU) and an attention mechanism (Attention Mode), establishing a hybrid neural network; then combining an adaptive enhancement integration algorithm (Adaboost) with the hybrid neural network to form a load prediction model; and evaluating power, gas, heat and cold load data of a certain integrated energy system by using the constructed load prediction model. Compared with the prior art, the method is higher in precision, and better in robustness and generalization of prediction of different types of loads of the integrated energy system.
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