Advances in Wind and Wind Power Forecasting and Diagnostics
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".
Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 9651
Special Issue Editor
Special Issue Information
Dear Colleagues,
Wind forecasting can be carried out utilizing a number of methods. Wind forecasts can come from numerical prediction models where the dynamical and thermodynamical variables are solved from a set of coupled partial differential equations based on the principles of geophysical fluid dynamics and thermodynamics or empirical models using statistics or machine learning. Additionally, wind forecasts can be derived by combining numerical weather forecasts and statistical or machine learning models.
Wind diagnostics may employ methods to characterize the temporal and spatial variabilities using methods such as Fourier analysis. In addition, machine learning methods such as self-organizing maps have been used to identify different wind regimes.
Many countries are adopting renewable energy, such as wind energy, in order to reduce the consumption of fossil fuels to combat pollution and climate change. However, accurate prediction of wind speed/power is essential in power grid integration due to the intermittent nature of wind power. Wind speed forecasts from numerical weather prediction models have been used to provide wind power forecasts by using the power curves or diagnostic relationship between wind speed and wind power. Machine learning models have also been used with numerical weather forecasts to provide wind power forecasts.
Manuscripts on all aspects of wind and wind power forecasting and diagnostics are welcome for this Special Issue.
Dr. William Cheng
Guest Editor
Manuscript Submission Information
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