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An Industrial Photovoltaic Prediction Model Based on Probabilistic Sparse Attention Mechanism of Temporal Convolution Network

In: Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)

Author

Listed:
  • Na Zhang

    (Liaoning Technical University, School of Business Administration)

  • Shichang Lu

    (Liaoning Technical University, School of Business Administration)

Abstract

This paper presents an advanced predictive model, termed C-PASST, which synergizes signal decomposition, sophisticated deep learning algorithms, and cutting-edge optimization techniques to enhance the accuracy of short-term power forecasts for photovoltaic systems. The process commences with the dissection of original photovoltaic data sequences through a comprehensive empirical modal decomposition method augmented by adaptive noise (C-DAN), adept at distilling temporal characteristics through a probabilistic sparse self-attention framework. Following this, the refined photovoltaic sequences are entrusted to specialized temporal convolutional networks (TCN) for prognostication. In the final stage, an innovative multiple universe optimizer (MVO) approach, informed by the principles of NNCT, is harnessed to integrate weight coefficients derived from the TCN models, culminating in the reconstruction of the ultimate forecasting outcomes.

Suggested Citation

  • Na Zhang & Shichang Lu, 2024. "An Industrial Photovoltaic Prediction Model Based on Probabilistic Sparse Attention Mechanism of Temporal Convolution Network," Advances in Economics, Business and Management Research, in: Valentin Vasilev & Cătălin Popescu & Yanhong Guo & Xiaolin Li (ed.), Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024), pages 1309-1315, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-570-6_131
    DOI: 10.2991/978-94-6463-570-6_131
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