An Integrated Digital-Twin-Assisted Edge Collaboration and LSTM-GAT Model for Short-Term Power Load Forecasting in Industrial Parks
DOI:
https://doi.org/10.4108/eetsis.14155Keywords:
Industrial Internet of Things, Digital Twin, Edge Computing, Industrial Park Distribution, Short-Term Power Load Forecasting, Energy Management, LSTM-GATAbstract
INTRODUCTION: Manufacturing-oriented industrial parks are increasingly characterised by flexible production lines, energy-intensive equipment, and highly coupled production–energy processes. In such smart manufacturing environments, accurate short-term power load forecasting is essential for coordinating production scheduling, equipment operation, and low-carbon energy management. However, existing forecasting methods often overlook edge-side training efficiency and spatial correlations among multi-node power loads.
OBJECTIVES: This paper aims to develop an efficient and accurate short-term power load forecasting framework for Industrial Internet of Things-enabled industrial parks by integrating digital-twin-assisted edge collaborative training with temporal–spatial load modelling.
METHODS: An integrated digital-twin-assisted mobile edge computing–Industrial Internet of Things framework and an LSTM-GAT forecasting model, named L-Ga, are proposed. The digital twin layer mirrors the communication, computing, energy, and data-quality states of edge nodes to support node selection and collaborative model training. The L-Ga model combines long short-term memory networks and graph attention networks to capture temporal evolution and spatial coupling among park-level, feeder-level, utility-system, office-area, and key-equipment loads.
RESULTS: The digital-twin-assisted node-selection strategy reduced the weighted training cost by 21.63% compared with random node selection. The proposed L-Ga model achieved average mean absolute error, root mean square error, and mean absolute percentage error values of 2.29, 3.12, and 1.98%, respectively, outperforming the representative baseline models.
CONCLUSION: The proposed framework improves both edge-side collaborative training efficiency and short-term power load forecasting accuracy. It provides an effective solution for production–energy coordination, intelligent power distribution, and low-carbon energy management in Industrial Internet of Things-enabled manufacturing parks.
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Copyright (c) 2026 Li Ruilin, honghong Li, Yoke Seng Wong, Fengyang Long, Donghua Jiang, Jianhua Wu
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