Work

Improving CNN Generalization for Photovoltaic Nowcasting Under Data Scarcity Through Sky Image Hybrid Augmentation Approaches

Year: 2026

Type: article

Abstract: Reliable photovoltaic (PV) power forecasting based on deep learning typically requires large historical datasets to capture the high temporal and spatial variability of solar irradiance. However, in many real-world applications, data availability is limited to short observation periods, hindering the effective training of deep learning models. This paper investigates how sky image data augmentation techniques can improve the generalization capability of Convolutional Neural Networks (CNNs) trained under data scarcity. Three augmentation-based oversampling methods—SMOTE, Mixup-kNN, and Mixup-RP—are evaluated, along with two novel hybrid strategies that combine these methods in parallel and series configurations. The proposed framework is validated on two distinct PV power nowcasting case studies, in which the original sky image training datasets span less than one month. Experimental results show average performance improvements of up to 50% on external testing data when training the CNN on the augmented datasets compared to the original base datasets, demonstrating that accurate PV power nowcasting is feasible even under data-scarce conditions typical of newly installed PV systems, and highlighting the potential of data-efficient learning approaches for renewable energy applications.

Source: Electronics

DOI: 10.3390/electronics15102054

Authors: Markos A. Kousounadis-Knousen, Velissarios Theocharis, Athina P. Georgilaki, Pavlos S. Georgilakis

Institution: National Technical University of Athens

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896