Temporal structure-preserving transformer for industrial load forecasting
View abstract on PubMed
Summary
This summary is machine-generated.Accurate industrial power load forecasting is improved with the Temporal Structure-Preserving Transformer (TSPT). This novel model effectively handles complex, multi-target data and integrates external factors for better energy management.
Area Of Science
- Energy Systems Engineering
- Artificial Intelligence
- Data Science
Background
- Industrial park energy management requires accurate power load forecasting.
- Existing models face challenges with multi-target load series and integrating diverse exogenous variables.
- Optimizing energy efficiency and operational decisions hinges on precise forecasting.
Purpose Of The Study
- To introduce a novel architecture, the Temporal Structure-Preserving Transformer (TSPT), for industrial power load forecasting.
- To address the limitations of existing models in handling complex, multi-target series and integrating exogenous data.
- To enhance forecasting accuracy by preserving temporal structures and fusing multiscale patterns.
Main Methods
- Decomposition of multi-target series into univariate series for parallel processing.
- Integration of exogenous variables (weather, production, efficiency data) using Gated Feature Fusion (GFF).
- Application of structure-preserving transformations to capture multiscale temporal patterns.
Main Results
- TSPT demonstrated superior performance compared to state-of-the-art methods on a real-world industrial park dataset.
- The model effectively handled complex, multi-target forecasting tasks with integrated exogenous variables.
- Preserving temporal structure and parallel processing significantly enhanced forecasting accuracy.
Conclusions
- TSPT offers a scalable and accurate solution for industrial power load forecasting.
- The proposed method improves energy management and operational decision-making in industrial settings.
- Effective integration of domain-specific knowledge enhances forecasting capabilities.
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