scholarly journals Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

2017 ◽  
Vol 32 (6) ◽  
pp. 2217-2227 ◽  
Author(s):  
Siri Sofie Eide ◽  
John Bjørnar Bremnes ◽  
Ingelin Steinsland
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Model Averaging ◽  
Strong Wind ◽  
Ensemble Forecasts ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
The One

Abstract In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

scholarly journals Wind Power potential in Kigali and Western provinces of Rwanda

2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Wind Power ◽  
Wind Direction ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Low Wind Speeds

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.

Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

2013 ◽  
Vol 141 (6) ◽  
pp. 2107-2119 ◽  
Author(s):  
J. McLean Sloughter ◽  
Tilmann Gneiting ◽  
Adrian E. Raftery
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Models ◽  
Weather Prediction ◽  
Model Averaging ◽  
Wind Vector ◽  
Bivariate Distributions ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Wide Range

Abstract Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting problems, which deal with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate distributions. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts’ relative contributions to predictive skill over a training period. In this paper the authors extend the BMA methodology to use bivariate distributions, enabling them to provide probabilistic forecasts of wind vectors. The BMA method is applied to 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble and is shown to provide better-calibrated probabilistic forecasts than the raw ensemble, which are also sharper than probabilistic forecasts derived from climatology.

Regime-dependent statistical post-processing of wind speed forecasts

2020 ◽  
Author(s):  
Sam Allen ◽  
Chris Ferro ◽  
Frank Kwasniok
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lead Times ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Atmospheric Flow ◽  
Wind Speeds ◽  
Systematic Biases ◽  
Processing Techniques

<p>Raw output from deterministic numerical weather prediction models is typically subject to systematic biases. Although ensemble forecasts provide invaluable information regarding the uncertainty in a prediction, they themselves often misrepresent the weather that occurs. Given their widespread use, the need for high-quality wind speed forecasts is well-documented. Several statistical approaches have therefore been proposed to recalibrate ensembles of wind speed forecasts, including a heteroscedastic censored regression approach. An extension to this method that utilises the prevailing atmospheric flow is implemented here in a quasigeostrophic simulation study and on reforecast data. It is hoped that this regime-dependent framework can alleviate errors owing to changes in the synoptic-scale atmospheric state. When the wind speed strongly depends on the underlying weather regime, the resulting forecasts have the potential to provide substantial improvements in skill upon conventional post-processing techniques. This is particularly pertinent at longer lead times, where there is more improvement to be gained upon current methods, and in weather regimes associated with wind speeds that differ greatly from climatology. In order to realise this potential, however, an accurate prediction of the future atmospheric regime is required.</p>

scholarly journals Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction

2010 ◽  
Vol 138 (5) ◽  
pp. 1811-1821 ◽  
Author(s):  
Le Bao ◽  
Tilmann Gneiting ◽  
Eric P. Grimit ◽  
Peter Guttorp ◽  
Adrian E. Raftery
Keyword(s):  
Wind Direction ◽  
Bias Correction ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Wind ◽  
Model Averaging ◽  
Forecast Performance ◽  
Correction Technique

Abstract Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical weather prediction models, which is based on a state-of-the-art circular–circular regression approach. To calibrate forecast ensembles, a Bayesian model averaging scheme for directional variables is introduced, where the component distributions are von Mises densities centered at the individually bias-corrected ensemble member forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific Northwest, using the University of Washington mesoscale ensemble, where they yield consistent improvements in forecast performance.

scholarly journals Harmonic oscillatory orientation relative to the wind in nocturnal roosting flights of the swift Apus apus

2002 ◽  
Vol 205 (7) ◽  
pp. 905-910 ◽  
Author(s):  
Johan Bäckman ◽  
Thomas Alerstam
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Harmonic Oscillation ◽  
Autocorrelation Analysis ◽  
Regular Feature ◽  
Wind Speeds ◽  
Heading Direction ◽  
Long Duration ◽  
Low Wind Speeds ◽  
Tracking Radar

SUMMARY Swifts regularly spend the night flying at high altitude. From previous studies based on tracking radar observations, we know that they stay airborne during the night and prefer to orient themselves into the wind direction with an increased angular concentration with increasing wind speed. In this study,we investigated the orientation relative to the wind of individual swifts by frequency (discrete Fourier transform) and autocorrelation analysis based on time series (10s intervals) of the angle between the swifts' heading and the wind direction for radar trackings of long duration (9-60 min). The swifts often showed a significant harmonic oscillation of their heading direction relative to the wind, with a frequency mostly in the range 1-17 mHz,corresponding to cycle periods of 1-16 min. The swifts also sometimes performed circling flights at low wind speeds. Wind speed ranged from 1.3 to 14.8 m s-1, and we expected to find different patterns of orientation at different wind speeds, assuming that the swifts adapt their orientation to avoid substantial displacement during their nocturnal flights. However, oscillatory orientation was found at all wind speeds with variable frequencies/periods that did not show any consistent relationship with wind speed. It remains to be shown whether cyclic heading changes are a regular feature of bird orientation.

Roughness sub-layer wind speed model for tropical wooded areas

2022 ◽  
pp. 0309524X2110500
Author(s):  
Gustavo Richmond-Navarro ◽  
Mariana Montenegro-Montero ◽  
Pedro Casanova-Treto ◽  
Franklin Hernández-Castro ◽  
Jorge Monge-Fallas
Keyword(s):  
Artificial Intelligence ◽  
Wind Speed ◽  
Functional Relationship ◽  
Polynomial Function ◽  
Evolutionary Search ◽  
Search Technique ◽  
Humidity Sensors ◽  
Wind Speeds ◽  
The One ◽  
Future Work

There are few reports in the literature regarding wind speed near the ground. This work presents a model for wind speed from 4 m above the ground, based on year-round measurements in two meteorological towers. Each tower is equipped with anemometers at five heights, as well as thermometers and pressure and relative humidity sensors. The data is processed using Eureqa artificial intelligence software, which determines the functional relationship between variables using an evolutionary search technique called symbolic regression. Using this technique, models are found for each month under study, in which height and temperature are the variables that most affect wind speed. The model that best predicts the measured wind speeds is then selected. A polynomial function directly proportional to height and temperature is identified as the one that provides the best predictions of wind speed on average, within the rough sub-layer. Finally, future work is identified on testing the model at other locations.

scholarly journals Catch Characteristics of Precipitation Gauges in High-Latitude Regions with High Winds

2006 ◽  
Vol 7 (5) ◽  
pp. 984-994 ◽  
Author(s):  
Konosuke Sugiura ◽  
Tetsuo Ohata ◽  
Daqing Yang
Keyword(s):  
Wind Speed ◽  
High Latitude ◽  
Cold Climate ◽  
Strong Wind ◽  
Blowing Snow ◽  
High Wind ◽  
Wind Speeds ◽  
Solid Precipitation ◽  
High Winds ◽  
Correction Equations

Abstract Intercomparison of solid precipitation measurement at Barrow, Alaska, has been carried out to examine the catch characteristics of various precipitation gauges in high-latitude regions with high winds and to evaluate the applicability of the WMO precipitation correction procedures. Five manual precipitation gauges (Canadian Nipher, Hellmann, Russian Tretyakov, U.S. 8-in., and Wyoming gauges) and a double fence intercomparison reference (DFIR) as an international reference standard have been installed. The data collected in the last three winters indicates that the amount of solid precipitation is characteristically low, and the zero-catch frequency of the nonshielded gauges is considerably high, 60%–80% of precipitation occurrences. The zero catch in high-latitude high-wind regions becomes a significant fraction of the total precipitation. At low wind speeds, the catch characteristics of the gauges are roughly similar to the DFIR, although it is noteworthy that the daily catch ratios decreased more rapidly with increasing wind speed compared to the WMO correction equations. The dependency of the daily catch ratios on air temperature was confirmed, and the rapid decrease in the daily catch ratios is due to small snow particles caused by the cold climate. The daily catch ratio of the Wyoming gauge clearly shows wind-induced losses. In addition, the daily catch ratios are considerably scattered under strong wind conditions due to the influence of blowing snow. This result suggests that it is not appropriate to extrapolate the WMO correction equations for the shielded gauges in high-latitude regions for high wind speed of over 6 m s−1.

scholarly journals Strategies for Enhancing the Low Wind Speed Performance of H-Darrieus Wind Turbine—Part 1

2019 ◽  
Vol 1 (1) ◽  
pp. 185-204 ◽  
Author(s):  
Palanisamy Mohan Kumar ◽  
Krishnamoorthi Sivalingam ◽  
Teik-Cheng Lim ◽  
Seeram Ramakrishna ◽  
He Wei
Keyword(s):  
Wind Speed ◽  
Poor Performance ◽  
Vertical Axis ◽  
Speed Ratio ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Viable Solution ◽  
Speed Performance ◽  
Low Wind Speeds ◽  
Starting Characteristics

Small wind turbines are key devices for micro generation in particular, with a notable contribution to the global wind energy sector. Darrieus turbines, despite being highly efficient among various types of vertical axis turbines, received much less attention due to their starting characteristics and poor performance in low wind speeds. Radically different concepts are proposed as a potential solution to enhance the performance of Darrieus turbine in the weak wind flows, all along the course of Darrieus turbine development. This paper presents a comprehensive review of proposed concepts with the focus set on the low wind speed performance and critically assessing their applicability based on economics, reliability, complexity, and commercialization aspects. The study is first of its kind to consolidate and compare various approaches studied on the Darrieus turbine with the objective of increasing performance at low wind. Most of the evaluated solutions demonstrate better performance only in the limited tip speed ratio, though they improve the low wind speed performance. Several recommendations have been developed based on the evaluated concepts, and we concluded that further critical research is required for a viable solution in making the Darrieus turbine a low speed device.

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 Numerical Simulation of the Aeroelastic Response of Wind Turbines in Typhoons Based on the Mesoscale WRF Model

2019 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Long Wang ◽  
Cheng Chen ◽  
Tongguang Wang ◽  
Weibin Wang
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Flow Direction ◽  
Wrf Model ◽  
Normal Operation ◽  
Average Wind Speed ◽  
Aeroelastic Response ◽  
Response Characteristics ◽  
Wind Speeds

A new simulation method for the aeroelastic response of wind turbines under typhoons is proposed. The mesoscale Weather Research and Forecasting (WRF) model was used to simulate a typhoon’s average wind speed field. The measured power spectrum and inverse Fourier transform method were coupled to simulate the pulsating wind speed field. Based on the modal method and beam theory, the wind turbine model was constructed, and the GH-BLADED commercial software package was used to calculate the aerodynamic load and aeroelastic response. The proposed method was applied to assess aeroelastic response characteristics of a commercial 6 MW offshore wind turbine under different wind speeds and direction variation patterns for the case study of typhoon Hagupit (2008), with a maximal wind speed of 230 km/h. The simulation results show that the typhoon’s average wind speed field and turbulence characteristics simulated by the proposed method are in good agreement with the measured values: Their difference in the main flow direction is only 1.7%. The scope of the wind turbine blade in the typhoon is significantly larger than under normal wind, while that under normal operation is higher than that under shutdown, even at low wind speeds. In addition, an abrupt change in wind direction has a significant impact on wind turbine response characteristics. Under normal operation, a sharp variation of the wind direction by 90 degrees in 6 s increases the wind turbine (WT) vibration scope by 27.9% in comparison with the case of permanent wind direction. In particular, the maximum deflection of the wind tower tip in the incoming flow direction reaches 28.4 m, which significantly exceeds the design standard safety threshold.

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