Meteorological data and Sky Images meets Neural Models for Photovoltaic Power Forecasting

2026-02-17

AI Summary

This study addresses the challenges of low prediction accuracy and poor detection of ramp events in photovoltaic (PV) power forecasting under cloudy conditions. To this end, the authors propose a multimodal deep neural network that integrates sky images, high-resolution meteorological variables—including surface longwave and shortwave radiation and wind speed—and solar position information. By jointly modeling these heterogeneous data sources, the model enables coordinated short-term nowcasting and medium-to-long-term forecasting of PV power output. Experimental results demonstrate that the proposed approach significantly improves both nowcasting and forecasting accuracy, with particularly notable enhancements in capturing ramp events under cloudy skies. These advances contribute to greater reliability in grid dispatch and PV system operation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Due to the rise in the use of renewable energies as an alternative to traditional ones, and especially solar energy, there is increasing interest in studying how to address photovoltaic forecasting in the face of the challenge of variability in photovoltaic energy production, using different methodologies. This work develops a hybrid approach for short and long-term forecasting based on two studies with the same purpose. A multimodal approach that combines images of the sky and photovoltaic energy history with meteorological data is proposed. The main goal is to improve the accuracy of ramp event prediction, increase the robustness of forecasts in cloudy conditions, and extend capabilities beyond nowcasting, to support more efficient operation of the power grid and better management of solar variability. Deep neural models are used for both nowcasting and forecasting solutions, incorporating individual and multiple meteorological variables, as well as an analytical solar position. The results demonstrate that the inclusion of meteorological data, particularly the surface long-wave, radiation downwards, and the combination of wind and solar position, significantly improves current predictions in both nowcasting and forecasting tasks, especially on cloudy days. This study highlights the importance of integrating diverse data sources to improve the reliability and interpretability of solar energy prediction models.

Problem

• Photovoltaic power forecasting
• Ramp event prediction
• Cloudy conditions
• Nowcasting
• Solar variability

Innovation

• multimodal fusion
• photovoltaic forecasting
• sky images
• meteorological data
• deep neural models