This paper presents an online platform that shows Thailand's solar irradiance map every 30 minutes. It is 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 evaluating 15-minute ground GHI data from 53 ground stations over 1.5 years from 2022-2023. The results show that the four models have competitive performances and outperform the service X. The best model is LightGBM, with an MAE of 78.58 W/sqm and RMSE of 118.97 W/sqm. Obtaining re-analyzed MERRA-2 data for Thailand is not economically feasible for deployment. When removing these features, the Informer model has a winning performance of 78.67 W/sqm in MAE. 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.

翻译：本文介绍了一个在线平台，该平台每30分钟更新展示泰国太阳辐照度地图，可通过 https://www.cusolarforecast.com 访问。估算泰国全境水平面总辐照度的方法依赖于从Himawari-8卫星影像中提取的云指数、采用本地调谐的Linke浑浊度系数的Ineichen晴空模型，以及机器学习模型。这些方法以晴空辐照度、云指数、来自MERRA-2数据库的再分析GHI与温度数据，以及日期时间作为GHI估算模型的输入，所使用的模型包括LightGBM、LSTM、Informer和Transformer。通过评估2022年至2023年1.5年间来自53个地面站的15分钟地面GHI数据，将上述模型与商业服务X的估算结果进行了基准比较。结果表明，四种模型均具有有竞争力的性能，且优于服务X。最佳模型为LightGBM，其平均绝对误差为78.58 W/㎡，均方根误差为118.97 W/㎡。为泰国获取再分析的MERRA-2数据对于实际部署而言经济上不可行。当移除这些特征时，Informer模型以78.67 W/㎡的MAE取得了最佳性能。所得性能在考虑气候带和数据时间粒度的情况下，与现有文献报道的结果一致。由于该地图展示了对超过93,000个网格的GHI估算并频繁更新，本文还描述了用于显示整个地图的计算框架，并测试了深度学习模型在GHI估算过程中的运行时性能。