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

TL;DR

This paper presents a hybrid neural approach combining sky images, meteorological data, and solar position to enhance short and long-term photovoltaic power forecasting, especially under cloudy conditions.

Contribution

It introduces a multimodal deep neural model integrating diverse data sources for improved photovoltaic energy prediction accuracy.

Findings

Meteorological data significantly improves forecast accuracy.

Combining sky images with meteorological data enhances robustness.

Model performs well under cloudy conditions.

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.