用新模型提升风电光伏长期预测精度，助力电网稳定运行
CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting
- 将通道与时间分块融合，捕捉多源能源的时空关联
- 在丹麦真实数据上优于现有方法，显著提升长期预测准确率
- 适合电力系统规划、储能调度等需要精准预判的场景
准确预测可再生能源发电量是提升现代电网动态性能的关键，尤其在高渗透率背景下。本文提出通道-时间分块时序变压器（CT-PatchTST），一种新型深度学习模型，用于实现风能与太阳能发电的长期高保真预测。与传统时序模型不同，该模型同时捕获时间依赖性和通道间相关性，对储能系统规划、控制与调度至关重要。可靠预测可实现储能系统的主动部署，缓解可再生能源输出不确定性，缩短系统响应时间，并根据地理位置的潮流与电压条件优化储能运行。在丹麦离岸风电、陆上风电及光伏发电的真实数据集上评估表明，CT-PatchTST在精度和鲁棒性上均优于现有方法。通过实现源-网-荷-储一体化系统中储能的预测性、数据驱动协同，本研究为构建更稳定、响应更快、成本更低的电力网络提供支持。
原文摘要 · Abstract (English)
Accurate forecasting of renewable energy generation is fundamental to enhancing the dynamic performance of modern power grids, especially under high renewable penetration. This paper presents Channel-Time Patch Time-Series Transformer (CT-PatchTST), a novel deep learning model designed to provide long-term, high-fidelity forecasts of wind and solar power. Unlike conventional time-series models, CT-PatchTST captures both temporal dependencies and inter-channel correlations-features that are critical for effective energy storage planning, control, and dispatch. Reliable forecasting enables proactive deployment of energy storage systems (ESSs), helping to mitigate uncertainties in renewable output, reduce system response time, and optimize storage operation based on location-specific flow and voltage conditions. Evaluated on real-world datasets from Denmark's offshore wind, onshore wind, and solar generation, CT-PatchTST outperforms existing methods in both accuracy and robustness. By enabling predictive, data-driven coordination of ESSs across integrated source-grid-load-storage systems, this work contributes to the design of more stable, responsive, and cost-efficient power networks.
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