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A Smart Control Method for Short-Term Load of Electricity Meters Based on TCN-BiGRU Attention Network and Model Predictive Control

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DOI: 10.23977/autml.2026.070204 | Downloads: 1 | Views: 80

Author(s)

Zhuoyue Xu 1, Wencan Huang 1

Affiliation(s)

1 TUT Maritime College, Tianjin University of Technology, Tianjin, China

Corresponding Author

Wencan Huang

ABSTRACT

To address the problems of prediction lag, insufficient identification of sudden load changes, and untimely response of control strategies in short-term load control of electricity meters, this paper proposes an intelligent load control method based on Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), Attention Mechanism (AM), and Model Predictive Control (MPC). This method takes the historical load sequence of the electricity meter as input. First, it uses the TCN to extract local fluctuation and periodic variation features. Then, it uses BiGRU to model the bidirectional temporal dependency of the load sequence and combines AM to enhance the expression of key time step information. Based on this, the predicted load is input into the model predictive control module for rolling optimization to generate real-time control commands. Experimental results show that the proposed method outperforms the comparative model in terms of prediction accuracy, error stability, and load control effect, effectively reducing peak load fluctuations and improving the real-time performance and reliability of electricity meter load regulation.

KEYWORDS

Temporal Convolutional Network; Bidirectional Gated Recurrent Unit; Short-Term Load Forecasting; Model Predictive Control

CITE THIS PAPER

Zhuoyue Xu, Wencan Huang. A Smart Control Method for Short-Term Load of Electricity Meters Based on TCN-BiGRU Attention Network and Model Predictive Control. Automation and Machine Learning (2026). Vol. 7, No. 2, 24-38. DOI: http://dx.doi.org/10.23977/autml.2026.070204.

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