Deep Learning Multi-Horizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images

Abstract

We investigate three distinct methods of incorporating all-sky imager (ASI) images into deep learning (DL) irradiance nowcasting. The first method relies on a convolutional neural network (CNN) to extract features directly from raw RGB images. The second method uses state-of-the-art algorithms to engineer 2D feature maps informed by domain knowledge, e.g., cloud segmentation, the cloud motion vector, solar position, and cloud base height. These feature maps are then passed to a CNN to extract compound features. The final method relies on aggregating the engineered 2D feature maps into time-series input. Each of the three methods were then used as part of a DL model trained on a high-frequency, 29-day dataset to generate multi-horizon forecasts of global horizontal irradiance up to 15 minutes ahead. The models were then evaluated using root mean squared error and skill score on 7 selected days of data. Aggregated engineered ASI features as model input yielded superior forecasting performance, demonstrating that integration of ASI images into DL nowcasting models is possible without complex spatially-ordered DL-architectures and inputs, underscoring opportunities for alternative image processing methods as well as the potential for improved spatial DL feature processing methods.

Figures (10)

• Figure 1: Map showing the locations of ASI 1 and ASI 2 and the pyranometer measuring GHI. The distance between the two cameras is highlighted, and the inset shows example time series data measured by the pyranometer from 13:30 CET to 14:20 CET on June 15th, 2023.
• Figure 2: Diagram of the three feature engineering and prediction methods A, B, and C. The output from either Method A, B, or C is concatenated with past GHI values as inputs into the LSTM-DNN bulk of the architecture, which is common for all three methods.
• Figure 3: Example image and feature maps extracted for ASI 1 at 2023-08-04 12:46:10 CET.
• Figure 4: Left panel shows the distribution of training and testing days with respect to time of year and the maximum GHI$_{clear}$. The gray shaded line indicates days where the maximum solar elevation is below 15$^{\circ}$. The right panel shows the distribution of training and testing days variability and clearness index with respect to 465 other days between 2022-06-09 and 2024-07-07.
• Figure 5: A comparison of RMSE and SS for models trained using the three methods shown in Fig. \ref{['fig:ABC_scheme']}.