Last edited by Megrel
Sunday, May 10, 2020 | History

1 edition of A comparison of predictors for first-guess wind speed errors found in the catalog.

A comparison of predictors for first-guess wind speed errors

Donald Paul Gaver

A comparison of predictors for first-guess wind speed errors

by Donald Paul Gaver

  • 236 Want to read
  • 0 Currently reading

Published by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va .
Written in English

    Subjects:
  • MATHEMATICAL MODELS,
  • MATHEMATICAL PREDICTION,
  • WIND VELOCITY

  • About the Edition

    Numerical meteorological models are used to assist in the prediction of weather. Each run of a numerical model produces forecasts of meteorological variables which are used as preliminary predictions of the future values of these variables. These initial predictions are referred to as first-guess values. Estimation of the mean-square first-guess error is required in the optimal interpolation process in the numerical prediction of atmospheric variables. Several predictors for the mean-square error of the first-guess wind speeds are studied. The results suggest that prediction using observed covariates tend to be better than those using first-guess covariates. However, observed covariates are not always available. Predictions using first-guess covariates are better at the 250 mb level than the 850 or 500 mb levels. Of those first-guess covariates studied, first-guess wind speed appears to be the best. Gaussian model with log-linear scale parameter, Nonparametric models, Prediction of mean square errors, First-guess errors in meteorological models, Generalized linear regression.

    Edition Notes

    Other titlesNPS-OR-93-020.
    StatementDonald P. Gaver, Patricia A. Jacobs
    ContributionsJacobs, Patricia A., Naval Postgraduate School (U.S.). Dept. of Operations Research
    The Physical Object
    Pagination33 p. ;
    Number of Pages33
    ID Numbers
    Open LibraryOL25510344M

    This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks Author: Saira Al-Zadjali, Ahmed Al Maashri, Amer Al-Hinai, Sultan Al-Yahyai, Mostafa Bakhtvar. wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based onreal forecast data.

      Wind energy is one such source and forecasting methods for the prediction of wind speed are becoming increasingly significant due to the penetration of wind power as an alternative to conventional energy sources. This paper proposes time series models for short-term prediction of wind by: When testing the ability of the neural network to learn in a wind speed prediction application, it was found that an artificial neural network can produce a good prediction of the wind speed. The prediction of the wind speed in the next 1 minute interval was found during the testing portion of the run to reach an average absolute difference of Author: Justin Tracy.

    Abstract This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based Cited by: advent of alternative energy, particularly wind power, necessitate the use of advanced tools for short-term prediction of wind speed or what is the same thing, the wind production. End-users (independent power producers, electrical companies, system operator distribution, etc.) which recognize the contribution of wind forecast for a safe and.


Share this book
You might also like
hedaya on gifts and wills

hedaya on gifts and wills

The history of the campaign in Germany, for the year 1704. Under the command of his grace John Duke of Marlborough, ...

The history of the campaign in Germany, for the year 1704. Under the command of his grace John Duke of Marlborough, ...

practical guide to testing object-oriented software

practical guide to testing object-oriented software

History of the United States

History of the United States

Soul-Winning Classics (The Fifty Greatest Christian Classics Series)

Soul-Winning Classics (The Fifty Greatest Christian Classics Series)

The Champlain Tercentenary: Report

The Champlain Tercentenary: Report

Pediatric education

Pediatric education

The Story for children

The Story for children

Pioneer surveyor, frontier lawyer

Pioneer surveyor, frontier lawyer

Julius Caesar

Julius Caesar

A time for survival

A time for survival

A comparison of predictors for first-guess wind speed errors by Donald Paul Gaver Download PDF EPUB FB2

Predictions using first-guess covariates are better at the mb level than the or mb levels. Of those first-guess covariates studied, first-guess wind speed appears to be the best.

A Comparison of predictors for first-guess wind speed errors RJH2G5 6. AUTHOR(S) Donald P. Gaver and Patricia A. Jacobs 7. PERFORMING ORGANIZATION NAME(S) AND ADORESS(ES) S. PERFORMING ORGANIZATION REPORT NUMBER Naval Postgraduate School Monterey, CA NPS-OR 9.

SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) The results suggest that prediction using observed covariates tend to be better than those using first-guess covariates. However, observed covariates are not always available.

Predictions using first-guess covariates are better at the mb level than the or mb : Donald Paul Gaver and Patricia A. Jacobs. A comparison of three methods for predicting wind speeds in complex forested terrain these techniques to the prediction of wind climate is most successful where the reference and the predicted tion of wind speed variation, underestimating hill top J C Suárez, B A Gardiner and P C Quine Cited by: the focus of this study is the statistical prediction of surface vector wind components and wind speed from Gaussian predictors.

Such predictors may be individual physical quantities or linear combinations of a number of these (as in a multiple linear regression).

For exam-ple, in the context of statistical downscaling, a predictor. Three layer model neural networks based on nonlinear prediction for long-term wind speed prediction presented in [43].Real wind speed data based on experimental results is applied for verification.

The evaluation result shows that for 50 % of tracing the error size is less than 5 % while the maximum error is 28 %. The prediction of wind speed plays a significant role in win d energy systems. An accurate pred iction of wind speed is more importa nt for wind energy systems, but it is difficult due to its.

and point wind speed forecasts using wind speed measurements (Klausner et al. ) also exhibit satisfactory results. Besides predicting the weather using past measure-ments or analyses, analogies can be employed to reduce the errors in the numerical weather prediction model simulations.

This approach utilizes achievements of. Two-year model for one day ahead (n = 24) prediction of wind speed or wind direction. Wind speed data series for the investigated period as recorded during the winter season of three successive years. Hybrid models incorporating GPR have not been extensively applied for wind speed prediction but there are a small number of previous studies.

Zhang et al. combine an autoregressive model with GPR for probabilistic wind speed forecasting. The model was used to predict mean hourly wind speed one hour ahead for wind speeds at 3 wind farms in by: predict an accurate and precise wind speed values.

This paper provides a recent review of wind speed and power prediction models previously presented in a literature and discuss the strengths and weakness of modeleach.

The rest of the paper is structured as follows. Section 2 present time scale concerning different wind speed prediction hori-zons.

Compared with point prediction, wind power interval prediction can quantify the range of changes in prediction results due to uncertain factors at a set of confidence levels that determine the predicted interval at the observed value, and can provide a comprehensive.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm by: 2. 1 The Gaussian Predictability of Wind Speeds n1 30 models uses statistical post-processing to correct errors in local wind predictions from 31 dynamicalmodels[insdottir and Gneiting,].

Direct Prediction of Wind Speed with a Single Gaussian Predictor. To further investigate the performance of the proposed hybrid models, an additional case using different wind speed samplings is provided in this study.

The forecasting results of the Case Two are presented in Fig. 19, Fig. 20, Fig. 21, Fig. 22, Fig. 23, Fig. 24, Fig. 25, Fig.

26, Fig. 27, Fig. 28, Fig. 29, Fig. Cited by: Evaluation of the mean errors obtained in the fold cross-validation tests for the year used to represent the short-term wind data period resulted in several conclusions.

These included, notably, that the WA gave lower mean errors than the FA in % of the cases analysed independently of Cited by: Solar radiation and temperature data for a two year period (–) in Sohar was used. The data contains hourly ambient temperature and global and diffuse solar radiation as shown in Fig.

figure shows that the global and diffused solar energy for Sohar (daily average) is W h/m 2 and W h/m 2, other words, the Sohar area can potentially be invested in with Cited by: The wind speed time-series of local point and its neighboring sites are employed to predict the wind speed– usually by NNs or adaptive neuro-fuzzy networks [17], [18].

In this study, the annual wind speed values of Kutahya, one of the regions in Turkey that has potential for wind energy at two different heights, were used and with the help of speed values at 10 m, wind speed values at 30 m of height were predicted by seven different machine learning by: 8.

Figure 1 Comparison of observed wind speed and predicted wind speed for 3, 6 and 12 hour ahead wind speed prediction with polynomial curve fitting models from “01/08/ ” to “16/12/ ”.

Auto-Regressive Moving Average (ARMA) models The second method of modeling which is used is the Auto-Regressive Moving-Average (ARMA) method. Spaceborne scatterometer data provide accurate information on speed and direction of surface wind over the global oceans.

Since the launch of the European Remote Sensing Satellite-1 (ERS-1) in Julyglobal coverage of scatterometer data has been available without ations vary from near-real-time assimilation into numerical weather prediction Cited by:   Abstract: Accurate forecasting of short-term wind speed is a key technology to enable efficient and reliable operation of microgrids with wind generators.

Results of wind speed prediction (WSP) methods in the current literature are subject to errors due to the random nature of wind speed and the limited generalization of forecast by:   For comparison to the satellite-derived wind fields, horizontal winds at a height of 10 m were obtained from a regional model yr run of REMO (Feser et al.

; Jacob et al. ).REMO is a gridpoint model using a rotated coordinates by: