RT info:eu-repo/semantics/article
T1 State of the Art for Solar and Wind Energy-Forecasting Methods for Sustainable Grid Integration
A1 Lafuente-Cacho, Marta
A1 Izquierdo-Monge, Oscar
A1 Peña-Carro, Paula
A1 Hernández-Jiménez, Ángel
A1 Callejo, Luis Hernández
A1 Losada, Ana María Palomares
A1 Lamadrid, Ángel Luis Zorita
AB 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.
PB Springer Nature
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/75699
UL https://uvadoc.uva.es/handle/10324/75699
LA eng
NO Current Sustainable/Renewable Energy Reports, (2025) 12:13
NO Producción Científica
DS UVaDOC
RD 09-may-2025