Domain-informed CNN architectures for downscaling regional wind forecasts
High-resolution wind speed forecasts are of great importance to the wind energy industry, from short-term energy forecasting and trading to longer-term resource assessment and planning. Generating high-resolution regional wind forecasts currently requires compute-intensive numerical models to downsc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000175 |
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| author | Alexander M. Campbell Simon C. Warder B. Bhaskaran Matthew D. Piggott |
| author_facet | Alexander M. Campbell Simon C. Warder B. Bhaskaran Matthew D. Piggott |
| author_sort | Alexander M. Campbell |
| collection | DOAJ |
| description | High-resolution wind speed forecasts are of great importance to the wind energy industry, from short-term energy forecasting and trading to longer-term resource assessment and planning. Generating high-resolution regional wind forecasts currently requires compute-intensive numerical models to downscale from a global forecast. Black-box AI models, once trained, can produce results in a fraction of the time and cost; however, they tend to produce smoothed outputs, are not interpretable and generalise poorly. The domain-informed AI architecture presented in this work seeks to address these problems by incorporating prior static fields directly into the model architecture. Specifically, the proposed approach combines two sequential U-Nets – the first upsamples the input wind fields and expands the number of feature maps, a fusion layer then injects prior static data such as topography, and a second U-Net generates the final output wind field. This approach improves all performance metrics versus a baseline U-Net model and generalises better to out-of-sample scenarios. In addition, this study compares the performance of several loss functions, including standard pixel-wise measures such as mean-squared error, structural similarity and frequency-focused functions, and a function based on Wiener filter theory. All loss functions, with the exception of the Wiener loss, perform comparably and tend to attenuate higher-frequency detail. Although the Wiener loss encourages higher frequencies, it over-estimates amplitudes. A composite Wiener-L1 loss function balances generating high-frequency detail and correctly predicting amplitudes. |
| format | Article |
| id | doaj-art-2825cded1bc042a6afc32e3fb13fac94 |
| institution | DOAJ |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-2825cded1bc042a6afc32e3fb13fac942025-08-20T03:11:06ZengElsevierEnergy and AI2666-54682025-05-012010048510.1016/j.egyai.2025.100485Domain-informed CNN architectures for downscaling regional wind forecastsAlexander M. Campbell0Simon C. Warder1B. Bhaskaran2Matthew D. Piggott3Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK; Corresponding author.Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UKShell Information Technology International Ltd, Shell Centre, London, SE1 7NA, UKDepartment of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UKHigh-resolution wind speed forecasts are of great importance to the wind energy industry, from short-term energy forecasting and trading to longer-term resource assessment and planning. Generating high-resolution regional wind forecasts currently requires compute-intensive numerical models to downscale from a global forecast. Black-box AI models, once trained, can produce results in a fraction of the time and cost; however, they tend to produce smoothed outputs, are not interpretable and generalise poorly. The domain-informed AI architecture presented in this work seeks to address these problems by incorporating prior static fields directly into the model architecture. Specifically, the proposed approach combines two sequential U-Nets – the first upsamples the input wind fields and expands the number of feature maps, a fusion layer then injects prior static data such as topography, and a second U-Net generates the final output wind field. This approach improves all performance metrics versus a baseline U-Net model and generalises better to out-of-sample scenarios. In addition, this study compares the performance of several loss functions, including standard pixel-wise measures such as mean-squared error, structural similarity and frequency-focused functions, and a function based on Wiener filter theory. All loss functions, with the exception of the Wiener loss, perform comparably and tend to attenuate higher-frequency detail. Although the Wiener loss encourages higher frequencies, it over-estimates amplitudes. A composite Wiener-L1 loss function balances generating high-frequency detail and correctly predicting amplitudes.http://www.sciencedirect.com/science/article/pii/S2666546825000175WeatherWind energyDownscalingCNN |
| spellingShingle | Alexander M. Campbell Simon C. Warder B. Bhaskaran Matthew D. Piggott Domain-informed CNN architectures for downscaling regional wind forecasts Energy and AI Weather Wind energy Downscaling CNN |
| title | Domain-informed CNN architectures for downscaling regional wind forecasts |
| title_full | Domain-informed CNN architectures for downscaling regional wind forecasts |
| title_fullStr | Domain-informed CNN architectures for downscaling regional wind forecasts |
| title_full_unstemmed | Domain-informed CNN architectures for downscaling regional wind forecasts |
| title_short | Domain-informed CNN architectures for downscaling regional wind forecasts |
| title_sort | domain informed cnn architectures for downscaling regional wind forecasts |
| topic | Weather Wind energy Downscaling CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000175 |
| work_keys_str_mv | AT alexandermcampbell domaininformedcnnarchitecturesfordownscalingregionalwindforecasts AT simoncwarder domaininformedcnnarchitecturesfordownscalingregionalwindforecasts AT bbhaskaran domaininformedcnnarchitecturesfordownscalingregionalwindforecasts AT matthewdpiggott domaininformedcnnarchitecturesfordownscalingregionalwindforecasts |