Cloud patterns clustering in geostationary satellite imagery: Investigating traditional and Deep-Learning-based feature extraction techniques for solar energy forecast

Forecasting solar energy is crucial for enabling its better integration into the energy mix. Short-term forecasts (up to 6h) have various applications, including grid integration and management, energy trading, energy storage management, and microgrid management. Photovoltaic (PV) production is highly variable and directly depends on solar irradiance reaching the Earth's surface. The stochastic component of solar irradiance variability is primarily related to its attenuation by the clouds.

Currently, cloud motion analysis methods applied to geostationary meteorological satellite images yield better results than Numerical Weather Prediction (NWP) models for short-term irradiance forecasting. Yet, their performance is limited in particular situations, such as convective or unstable cloud conditions. Deep Learning methods may overcome these limitations because of their ability to automatically extract complex spatiotemporal cloud patterns.

Prior to implementing a Deep Learning method, it is essential to analyse the data in order to understand its spatiotemporal characteristics. Clustering cloud patterns might allow us to better characterize cloud cover evolutions in distinct weather situations, thereby enabling the development of more accurate short-term solar energy forecasting models. Our research establishes a foundation for partition-based forecasts using multiple models, each model being adjusted to the distinct traits of each group.

In this work, we conduct a comparative analysis between traditional feature extraction techniques (e.g., statistical, textural, and temporal features) and Deep-Learning-based feature extraction (e.g., autoencoding) to evaluate their ability to discriminate specific cloud pattern evolutions (e.g., advections, convection, multi-layer, appearance/disappearance). Additionally, we assess and compare several clustering methods (e.g., K-Means and its variations, Hierarchical Clustering, Gaussian Mixture Model) using the extracted features as the input. Then, we evaluate the quality of the final partitioning using quantitative criteria and studying its physical consistency. Finally, we conduct an extensive study of the partitioning results, highlighting useful insights about the data such as the average duration of a given meteorological phenomenon. For this study, we use cloud albedo images derived from the HRV channel of the Meteosat Second Generation’s (MSG) SEVIRI instrument and focus on the Paris area.

The proposed feature extraction method enables us to perform clustering analysis that effectively distinguishes identifiable meteorological situations and cloud pattern evolutions. This is preliminary work for the development of a partition-based Deep Learning model for short-term solar energy forecasting, with the goal of addressing complex and diverse forecast scenarios using multiple models.