Developing a Thailand solar irradiance map using Himawari-8 satellite imageries and deep learning models
Suwichaya Suwanwimolkul, Natanon Tongamrak, Nuttamon Thungka, Naebboon Hoonchareon, Jitkomut Songsiri
TL;DR
This work develops near real-time, country-scale GHI maps for Thailand by fusing cloud indices derived from Himawari-8 imagery with a tuned Ineichen clear-sky model and data-driven ML estimators. The methodology benchmarks LightGBM, LSTM, Transformer, and Informer against ground-based GHI measurements across 53 sites, achieving MAEs around 78–82 W/m^2 and RMSEs comparable to a commercial service, while enabling 30-minute updates over 93,061 grids. A key contribution is the monthly optimization of the Linke turbidity TL for the Thailand climate, along with adaptations of Transformer/Informer for large-scale time-series forecasting, and a deployment framework that demonstrates real-time performance on modern GPUs. The resulting Thailand solar map supports grid planning and operation and offers a feasible, cost-effective alternative to commercial irradiance services, with code and data workflows described for reproducibility and extension.
Abstract
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.