Deep Learning for Solar Irradiance Nowcasting: A Comparison of a Recurrent Neural Network and Two Traditional Methods

Dennis Knol, Fons de Leeuw, Jan Fokke Meirink, Valeria V. Krzhizhanovskaya

This paper aims to improve short-term forecasting of clouds to accelerate the usability of solar energy. It compares the Convolutional Gated Recurrent Unit (ConvGRU) model to an optical flow baseline and the Numerical Weather Prediction (NWP) Weather Research and Forecast (WRF) model. The models are evaluated over 75 days in the summer of 2019 for an area covering the Netherlands, and it is studied under what circumstance the models perform best. The ConvGRU model proved to outperform both extrapolation-based methods and an operational NWP system in the precipitation domain. For our study, the model trains on sequences containing irradiance data from the Meteosat Second Generation Cloud Physical Properties (MSG-CPP) dataset. Additionally, we design an extension to the model, enabling the model also to exploit geographical data. The experimental results show that the ConvGRU outperforms the other methods in all weather conditions and improves the optical flow benchmark by 9%9% in terms of Mean Absolute Error (MAE). However, the ConvGRU prediction samples demonstrate that the model suffers from a blurry image problem, which causes cloud structures to smooth out over time. The optical flow model is better at representing cloud fields throughout the forecast. The WRF model performs best on clear days in terms of the Structural Similarity Index Metric (SSIM) but suffers from the simulation’s short-range.

Bibliografische gegevens

Dennis Knol, Fons de Leeuw, Jan Fokke Meirink, Valeria V. Krzhizhanovskaya. Deep Learning for Solar Irradiance Nowcasting: A Comparison of a Recurrent Neural Network and Two Traditional Methods
Year: 2021, Other information:

In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_24