2026-04-27T07:53:27Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/756992025-09-04T09:10:31Zcom_10324_1166com_10324_931com_10324_894col_10324_1338

Lafuente Cacho, Marta Izquierdo Monge, Óscar Peña Carro, Paula Hernández Jiménez, Ángel Hernández Callejo, Luis Palomares Losada, Ana María Zorita Lamadrid, Ángel Luis 2025-05-08T09:28:51Z 2025-05-08T09:28:51Z 2025 Current Sustainable/Renewable Energy Reports, (2025) 12:13 https://uvadoc.uva.es/handle/10324/75699 10.1007/s40518-025-00262-z 13 Current Sustainable/Renewable Energy Reports 12 2196-3010 Forecasting renewable energy generation is crucial for improving the efficiency and reliability of power systems that integrate wind, solar, and other renewable sources. These energy sources are inherently variable, depending on changing weather patterns, which makes accurate forecasting a complex task. The ability to predict renewable energy production with high accuracy can help grid operators optimize energy storage, reduce the reliance on fossil fuels, and ensure grid stability. Forecasting methods vary significantly, ranging from physical models that rely on weather data, to statistical models and advanced machine learning techniques. Each method has its own strengths and limitations, and the choice of approach often depends on the specific requirements, such as time horizon, data availability, and computational resources. eng info:eu-repo/semantics/openAccess State of the Art for Solar and Wind Energy-Forecasting Methods for Sustainable Grid Integration info:eu-repo/semantics/article