Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/133339
Título: A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used
Autores/as: Travieso-González, Carlos M. 
Cabrera-Quintero, Fidel 
Piñan Roescher, Alejandro 
Celada Bernal, Sergio 
Clasificación UNESCO: 33 Ciencias tecnológicas
Palabras clave: Numerical Weather Prediction
Automatic Cloud Classification
Machine Learning Techniques
Artificial Neural-Network
Irradiance Forecasts, et al.
Fecha de publicación: 2024
Publicación seriada: Applied Sciences (Basel) 
Resumen: The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.
URI: http://hdl.handle.net/10553/133339
DOI: 10.3390/app14135605
Fuente: Applied Sciences-Basel,v. 14 (13), (Julio 2024)
Colección:Artículos
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