Review on probabilistic forecasting of wind power generation
Y Zhang, J Wang, X Wang - Renewable and Sustainable Energy Reviews, 2014 - Elsevier
The randomness and intermittence of wind resources is the biggest challenge in the
integration of wind power into the power system. Accurate forecasting of wind power …
integration of wind power into the power system. Accurate forecasting of wind power …
A deep learning method for bias correction of ECMWF 24–240 h forecasts
L Han, M Chen, K Chen, H Chen, Y Zhang, B Lu… - … in Atmospheric Sciences, 2021 - Springer
Correcting the forecast bias of numerical weather prediction models is important for severe
weather warnings. The refined grid forecast requires direct correction on gridded forecast …
weather warnings. The refined grid forecast requires direct correction on gridded forecast …
Future behavior of wind wave extremes due to climate change
Extreme waves will undergo changes in the future when exposed to different climate change
scenarios. These changes are evaluated through the analysis of significant wave height (Hs) …
scenarios. These changes are evaluated through the analysis of significant wave height (Hs) …
ARMA based approaches for forecasting the tuple of wind speed and direction
E Erdem, J Shi - Applied Energy, 2011 - Elsevier
Short-term forecasting of wind speed and direction is of great importance to wind turbine
operation and efficient energy harvesting. In this study, the forecasting of wind speed and …
operation and efficient energy harvesting. In this study, the forecasting of wind speed and …
[ΒΙΒΛΙΟ][B] Statistical pattern recognition
AR Webb - 2003 - books.google.com
Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …
many advances in recent years. New andemerging applications-such as data mining, web …
[HTML][HTML] Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics
Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model
Output Statistics in: Monthly Weather Review Volume 144 Issue 6 (2016) Jump to Content …
Output Statistics in: Monthly Weather Review Volume 144 Issue 6 (2016) Jump to Content …
Uncertainty quantification in complex simulation models using ensemble copula coupling
R Schefzik, TL Thorarinsdottir, T Gneiting - 2013 - projecteuclid.org
Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
Page 1 Statistical Science 2013, Vol. 28, No. 4, 616–640 DOI: 10.1214/13-STS443 © Institute of …
Page 1 Statistical Science 2013, Vol. 28, No. 4, 616–640 DOI: 10.1214/13-STS443 © Institute of …
Control-oriented modelling of wind direction variability
Wind direction variability significantly affects the performance and lifetime of wind turbines
and wind farms. Accurately modelling wind direction variability and understanding the …
and wind farms. Accurately modelling wind direction variability and understanding the …
[HTML][HTML] On the ability of the WRF model to reproduce the surface wind direction over complex terrain
PA Jiménez, J Dudhia - Journal of Applied Meteorology and …, 2013 - journals.ametsoc.org
On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex
Terrain in: Journal of Applied Meteorology and Climatology Volume 52 Issue 7 (2013) Jump …
Terrain in: Journal of Applied Meteorology and Climatology Volume 52 Issue 7 (2013) Jump …
Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas
We propose a method for post‐processing an ensemble of multivariate forecasts in order to
obtain a joint predictive distribution of weather. Our method utilizes existing univariate post …
obtain a joint predictive distribution of weather. Our method utilizes existing univariate post …