Figure 3.

SurfUVNet accurately forecast antipsoriatic irradiance throughout the day. Results on Nakhon Pathom dataset were shown. (a) Comparison of the mean absolute percentage errors (MAPE) for the next-day antipsoriatic irradiance forecast between SurfUVNet (Seq2Seq-14) and four benchmark models (see “Methods” section). Previous day model simply predicts next-day’s UV radiation to be the same as today’s. Regression model refers to the regression model based on Earth–Sun distance and total ozone column currently in used by the Thai Meteorological Department²². BiGRU is an artificial neural network architecture that is often utilized for time series forecasting. CNN-LSTM, and CNN-LSTM-SG are artificial neural network models that were recently applied to UV forecasting in the energy domain²⁷. The tags − 7, − 14, and − 21 designate the length of UV data, in days prior to the forecast date, that were input into each model. (b) Distribution of MAPE for the validation set (UV data from 2018) throughout the times of the day. Results for the best performing models, namely CNN-LSTM-SG-7 and SurfUVNet (Seq2Seq-14), are shown. (c) A similar plot showing distribution of MAPE for the test set (UV data from 2019). (d) Comparison of ground truth UV data and forecasts made by SurfUVNet for the validation set (UV data from 2018). Error bars indicate one-standard deviation ranges. (e) A similar plot for the test set (UV data from 2019).