A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models

Abstract

Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.

This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. The FCM layer first groups meteorological data into coherent clusters, reducing noise and isolating localized patterns. CNNs then extract high-level spatial features from each cluster, while WNNs decode multi-resolution irradiance dynamics, capturing both abrupt fluctuations and gradual trends. Finally, the Informer model — equipped with attention mechanisms — identifies long-term temporal dependencies, selecting the most informative timesteps for accurate prediction.

The study’s experiments were conducted using a comprehensive dataset sourced from the Photovoltaic Geographical Information System (PVGIS). This dataset spans from January 1, 2005, to December 31, 2020. Data was collected from four climatically distinct cities in the USA: Phoenix, Arizona (desert); Miami, Florida (tropical); Denver, Colorado (semi-arid, high altitude); and Seattle, Washington (oceanic). The proposed CNN-WNN-Informer model achieved average reductions across all cities of 67.7% in t-statistic, 73.9% in Mean Absolute Percentage Error (MAPE), 82.5% in Mean Absolute Bias Error (MABE), and 59.0% in Root Mean Square Error (RMSE), underscoring its significant improvements. By minimizing prediction uncertainty, the framework empowers smarter battery utilization and route planning, bridging the gap between renewable energy and sustainable mobility. This robust performance suggests its potential for integration into intelligent transportation systems and smart grid applications, paving the way for more resilient and energy-efficient urban environments.