Scientific Reports - Article in Press
Fast-Powerformer: Accurate and Memory-Efficient Mid-Term Wind Power Forecasting
Published: 29 January 2026
Authors: Mingyi Zhu, Zhaoxing Li, Qiao Lin & Li Ding
This paper introduces Fast-Powerformer, a novel Transformer-based architecture designed for mid-term wind power forecasting. It addresses the critical trade-off between predictive accuracy and computational efficiency, crucial for resource-constrained environments. By integrating an Input Transposition Mechanism, a lightweight LSTM embedding, and a Frequency-Enhanced Channel Attention Mechanism (FECAM), Fast-Powerformer achieves superior accuracy with significantly reduced memory footprint and faster inference times compared to existing models.
Executive Impact
Revolutionizing Wind Power Prediction Efficiency
Fast-Powerformer sets new benchmarks for operational efficiency and predictive accuracy in mid-term wind power forecasting, enabling smarter energy grid management with significantly reduced computational overhead.
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Lowest sMAPE Score
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Reduced Peak GPU Memory
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Faster Epoch Time
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Fast-Powerformer Workflow
The Fast-Powerformer integrates three complementary strategies built upon a Reformer backbone for accurate and memory-efficient mid-term wind power forecasting.
Raw Data Preprocessing
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Lightweight LSTM Embedding
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Input Transposition (Variate-centric)
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Reformer Encoder (LSH Attention)
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Frequency-Enhanced Channel Attention (FECAM)
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Output Projection & Prediction
| Model | MSE | MAE | sMAPE |
|---|---|---|---|
| Fast-Powerformer | 0.854 | 0.632 | 0.481 |
| Autoformer | 0.848 | 0.656 | 0.498 |
| iTransformer | 0.867 | 0.660 | 0.523 |
| Informer | 0.987 | 0.700 | 0.491 |
| Reformer | 0.933 | 0.698 | 0.638 |
| Transformer | 0.898 | 0.664 | 0.613 |
| LSTM | 0.940 | 0.750 | 0.532 |
| MLP | 1.040 | 0.810 | 0.586 |
| ARIMA | 1.870 | 1.108 | 0.698 |
Fast-Powerformer demonstrates superior accuracy across key metrics against state-of-the-art models for mid-term wind power forecasting. |
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686MB
Lowest Peak GPU Memory Usage
Fast-Powerformer achieved the lowest peak GPU memory usage (686 MB) among all Transformer-based models, making it exceptionally well-suited for resource-constrained environments.
Robustness Across Diverse Wind Farms
Fast-Powerformer demonstrates strong generalization capability across wind farms with distinct physical and meteorological characteristics (Farm 2 and Farm 3). It consistently provides favorable accuracy-efficiency trade-offs, confirming its suitability for practical mid-term wind power forecasting in heterogeneous real-world environments.
- On Farm 2 (stronger winds, frequent peaks), Fast-Powerformer achieved the lowest sMAPE (0.221), demonstrating stable forecasting under high-variability.
- On Farm 3 (weaker winds, irregular variability), Fast-Powerformer delivered the best performance across all three metrics (MSE 0.750, MAE 0.677, sMAPE 0.312).
- Maintained high computational efficiency across all farms, with epoch times of 80s (Farm 2) and 65s (Farm 3), and memory usage of 720MB and 645MB respectively.
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