Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU

Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU

Abstract Accurately predicting solar power is essential for ensuring electric grid reliability and integrating renewable energy sources. This paper presents a novel approach to probabilistic solar power forecasting by combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) int...

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Bibliographic Details
Main Authors: Louiza Ait Mouloud, Aissa Kheldoun, Samira Oussidhoum, Hisham Alharbi, Saud Alotaibi, Thabet Alzahrani, Takele Ferede Agajie
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Solar power
Seasonal forecasting
Quantile probabilistic forecasting
Deep learning
Continuous ranked probability score (CRPS)
Reliability diagram
Online Access:https://doi.org/10.1038/s41598-025-12797-8
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https://doi.org/10.1038/s41598-025-12797-8

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