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ABSTRACT Accurate wind power forecasts are essential for energy management and resource allocation. However, because of complex weather dynamics and other nonlinearities, it is exceedingly difficult to forecast wind power on the multisite level for dozens of wind farms at once. This paper proposes a hybridized approach that leverages deep learning to predict future forecast errors from physics‐based numerical weather prediction (NWP) model estimates. Utilizing errors from NWP forecasts allows integration of critical atmospheric and meteorological dynamics into the forecasting model, and we demonstrate the importance of post‐calibration based on the physics versus pure data‐driven wind power prediction. This post‐calibration approach is enabled by the inverted transformer architecture, which efficiently and effectively learns meaningful wind farm variate representations, resulting in accurate spatiotemporal corrections to the forecasts. We also investigate modifying the iTransformer with a new embedding approach, named SpaceEmbed, that explicitly encodes spatial distance information into the network. The proposed approach is validated with a case study using real‐world data and forecasts from the Electric Reliability Council of Texas (ERCOT) in 2015 for 74 wind farms in Texas at different time scales. Using the high sustained limit as the metric for power generation, the iTransformer outperforms other state‐of‐the‐art deep learning forecasting methods, succeeding at the post‐calibration task by reducing NWP forecast error by up to 33% on average.
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This content will become publicly available on July 27, 2026
WindSR-4D: Super-Resolution Spatiotemporal Wind Forecasting at Wind Farm Scale
Abstract Existing weather prediction products suffer from low spatiotemporal resolution, which fails to meet the requirements for fine-grained power forecasting and intelligent control of wind farms. This limitation adversely impacts wind turbine load management and the operational reliability of power systems. To tackle this, the study explores four-dimensional wind field superresolution, aiming to reconstruct high-resolution wind fields that fulfill both temporal and spatial resolution requirements through deep learning methods. The study emphasizes model architecture innovations to effectively capture the spatiotemporal coupling characteristics of wind field variations within wind farm regions. A novel model, termed 4dST-DMS, is proposed, comprising a spatial module and a temporal module, both built upon a 3D extension of the downsampled skip-connection/multi-scale framework as the backbone. Additionally, an iterative interpolation prediction method is proposed in the temporal module to extract multi-scale temporal information and capture dynamic variations, ensuring high fidelity in the reconstructed wind field. The proposed method is validated using the WTK-LED-5min dataset under experimental settings, including a 10-fold spatial resolution enhancement (both longitude and latitude), a 4-fold height resolution enhancement, and a 12-fold temporal resolution enhancement. The results demonstrate that the 4dST-DMS outperforms conventional interpolation methods and existing deep learning approaches in terms of numerical accuracy, vector field continuity and smoothness, structural similarity to real wind fields, and physical fidelity.
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- Award ID(s):
- 2443363
- PAR ID:
- 10645629
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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