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MoADL-TLSTM: Multiobjective Automated Deep Learning-Based Transformer-LSTM for Load Forecasting.

Kang-Di Lu, Bing-Xu Zhang, Yong Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2026
    PubMed
    Summary
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    This study introduces a novel automated deep learning method for accurate load forecasting in power systems. The proposed model balances performance and complexity, outperforming existing techniques for energy allocation.

    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Artificial Intelligence

    Background:

    • Accurate load forecasting is crucial for optimizing energy allocation and economic operation in cyber-physical power systems (CPPSs).
    • Deep learning (DL) models excel at capturing temporal patterns but existing methods often rely on single models, lack automation, and are overly complex.
    • Manual DL model design requires significant domain expertise and struggles with balancing performance and complexity.

    Purpose of the Study:

    • To propose a novel multiobjective automated deep learning (MoADL) method for load forecasting.
    • To develop a hybrid and lightweight Transformer-Long Short-Term Memory (TLSTM) model automatically.
    • To balance model forecasting performance and complexity in cyber-physical power systems.

    Main Methods:

    Related Experiment Videos

    • Combined Transformer's global dependency modeling with LSTM's sequential processing for feature extraction.
    • Developed a nondominated sorting genetic algorithm II (NSGA-II)-based evolutionary mechanism for neural architecture and hyper-parameter optimization.
    • Utilized variable-length encoding, crossover, and mutation operations for evolving TLSTM models.

    Main Results:

    • The proposed MoADL-TLSTM method demonstrated superior forecasting performance compared to state-of-the-art methods.
    • The model achieved a better balance between forecasting accuracy and model complexity.
    • Experimental results on 12 real-world Australian load datasets validated the method's effectiveness.

    Conclusions:

    • The MoADL-TLSTM method offers an effective solution for automated, lightweight, and high-performance load forecasting.
    • This approach addresses limitations of existing DL models in CPPSs by enabling automatic hybrid model design.
    • The findings suggest a promising direction for improving energy management through advanced AI techniques.