用卫星和AI每半小时生成泰国太阳能辐照度地图，精度接近商用服务。
Developing a Thailand solar irradiance map using Himawari-8 satellite imageries and deep learning models
- 结合卫星云图、气象数据与深度学习模型预测太阳辐射。
- 轻量级LightGBM模型整体误差最小，达78.58W/m² MAE。
- 适合关注光伏规划与能源调度的政府及企业用户。
本文提出一个在线平台，每30分钟更新一次泰国全域太阳辐照度地图（https://www.cusolarforecast.com）。通过提取Himawari-8卫星影像的云指数，结合本地化调校的Linke浊度参数与Ineichen清空模型，利用再分析的MERRA-2气象数据（包括地表辐照度、温度与时间）作为输入，构建LightGBM、LSTM、Informer与Transformer等机器学习模型进行全球水平辐照度（GHI）估算。在2022–2023年期间，基于53个地面站点1.5年的15分钟实测数据进行评估，四类模型整体平均绝对误差（MAE）与商用服务X相当。其中，LightGBM表现最佳，整体MAE为78.58 W/m²，RMSE为118.97 W/m²；而商用服务在多云条件下性能最优。由于获取全境MERRA-2数据成本过高，移除该特征后，Informer模型仍保持优异表现，MAE为78.67 W/m²。地图覆盖93,000个网格，支持高频更新，论文还介绍了可视化计算框架，并测试了各深度学习模型的运行效率。
原文摘要 · Abstract (English)
This paper presents an online platform showing Thailand solar irradiance map every 30 minutes, available at https://www.cusolarforecast.com. The methodology for estimating global horizontal irradiance (GHI) across Thailand relies on cloud index extracted from Himawari-8 satellite imagery, Ineichen clear-sky model with locally-tuned Linke turbidity, and machine learning models. The methods take clear-sky irradiance, cloud index, re-analyzed GHI and temperature data from the MERRA-2 database, and date-time as inputs for GHI estimation models, including LightGBM, LSTM, Informer, and Transformer. These are benchmarked with the estimate from a commercial service X by evaluation of 15-minute ground GHI data from 53 ground stations over 1.5 years during 2022-2023. The results show that the four models exhibit comparable overall MAE performance to the service X. The best model is LightGBM with an overall MAE of 78.58 W/sqm and RMSE of 118.97 W/sqm, while the service X achieves the lowest MAE, RMSE, and MBE in cloudy condition. Obtaining re-analyzed MERRA-2 data for the whole Thailand region is not economically feasible for deployment. When removing these features, the Informer model has a winning performance in MAE of 78.67 W/sqm. The obtained performance aligns with existing literature by taking the climate zone and time granularity of data into consideration. As the map shows an estimate of GHI over 93,000 grids with a frequent update, the paper also describes a computational framework for displaying the entire map. It tests the runtime performance of deep learning models in the GHI estimation process.
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