scholarly journals Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

2014 ◽  
Vol 26 (2) ◽  
pp. 120-132 ◽  
Author(s):  
S. Baran ◽  
A. Möller
Keyword(s):  
Wind Speed ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Probabilistic Forecasting

Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method

2009 ◽  
Author(s):  
Gong Li ◽  
Jing Shi ◽  
Junyi Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term ◽  
Goodness Of Fit Test ◽  
Wind Speed Forecasting

Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.

scholarly journals Wind power application research on the fusion of the determination and ensemble prediction

2017 ◽  
Vol 14 ◽  
pp. 227-230
Author(s):  
Shi Lan ◽  
Xu Lina ◽  
Hao Yuzhu
Keyword(s):  
Wind Speed ◽  
Numerical Model ◽  
Wind Power ◽  
Wind Farm ◽  
Bayesian Model Averaging ◽  
Arima Model ◽  
Model Averaging ◽  
Absolute Error ◽  
Ensemble Prediction ◽  
Probabilistic Forecasting

Abstract. The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0–24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

scholarly journals A Probabilistic Forecast of Wind Speed using Bayesian Model Averaging

2016 ◽  
Vol 11 (3) ◽  
pp. 221-235
Author(s):  
Keunhee Han ◽  
◽  
Chansik Kim ◽  
Chansoo Kim
Keyword(s):  
Wind Speed ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Probabilistic Forecast

scholarly journals Impact of Bias-Correction Type and Conditional Training on Bayesian Model Averaging over the Northeast United States

2012 ◽  
Vol 27 (6) ◽  
pp. 1449-1469 ◽  
Author(s):  
Michael J. Erickson ◽  
Brian A. Colle ◽  
Joseph J. Charney
Keyword(s):  
United States ◽  
Wind Speed ◽  
Bias Correction ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Cumulative Distribution ◽  
Northeast United States ◽  
Fire Threat ◽  
Sequential Training

Abstract The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC. The ensemble is verified using 2-m temperature and 10-m wind speed for the 2007–09 warm seasons, and for subsets of days with high ozone and high fire threat. The impacts of training period, bias-correction method, and BMA are explored for these potentially hazardous weather events using the most recent consecutive (sequential training) and most recent similar days (conditional training). BMA sensitivity to the selection of ensemble members is explored. A running mean difference between forecasts and observations using the last 14 days is better at removing temperature bias than is a cumulative distribution function (CDF) or linear regression approach. Wind speed bias is better removed by adjusting the modeled CDF to the observation. High fire threat and ozone days exhibit a larger cool bias and a greater negative wind speed bias than the warm-season average. Conditional bias correction is generally better at removing temperature and wind speed biases than sequential training. Greater probabilistic skill is found for temperature using both conditional bias correction and BMA compared to sequential bias correction with or without BMA. Conditional and sequential BMA results are similar for 10-m wind speed, although BMA typically improves probabilistic skill regardless of training.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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