A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2016. / Wind sensors provide very accurate measurements, however it is not feasible to have a network of wind sensors large enough to provide these accurate readings everywhere. A “virtual” wind sensor uses existing weather forecasts, as well as historical weather station data to predict what readings a regular wind sensor would provide. This study attempts to develop a method using Big Data Analytics to predict wind readings for use in “virtual” wind sensors. The study uses Random Forests and linear regression to estimate wind direction and magnitude using various transformations of a Digital Elevation Model, as well as data from the European Centre for Medium-Range Weather Forecasts. The model is evaluated based on its accuracy when compared to existing high resolution weather station data, to show a slight improvement in the estimation of wind direction and magnitude over the forecast data. / LG2017

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model.

Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 320. Thesis director: Michael Summers. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 291-319). Also issued in print.

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Douglas K. Miller, Wendell A. Nuss. Includes bibliographical references (p. 125-128). Also available online.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 136-139).

Multiple linear regression was used to develop equations for 12-, 24-, and 36-hour surface wind forecasts for the wind energy site at Goodnoe Hills. Equations were derived separately for warm and cool seasons. The potential predictors included LFM II model output, MOS surface wind forecasts extrapolated from surrounding stations, pressure observations corrected to mean sea level, and two types of climatological variables. Forecasts of wind speed and direction were formulated for an independent sample of predictands and predictors. The forecasts were evaluated using standard methods of forecast verification and the results are summarized in terms of several verification scores. Comparisons of scores were made by season, projection time, and cycle (or preparation) time, and some patterns were evident in the scores with respect to these stratifications. The minimum value of the mean absolute error attained by the forecast system presented here was 5.64 mph for a 12-hour, cool season forecast equation. The minimum value of the root mean square error was 7.57 mph for a 12-hour, warm season forecast equation. Comparison of these results with the results of other statistical wind forecasting studies indicates that the forecast equations for Goodnoe Hills are of comparable accuracy to the equations developed for other wind energy sites. Suggestions for future investigations of statistical wind forecasting are offered as well as recommendations concerning ways of improving the forecasting system described in this study. / Graduation date: 1983

Thesis (M.S.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 11, 2008) Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin, 1961. / Also published as AFCRL-TN-61-844, and University of Wisconsin Dept. of Meteorology Scientific report no. 5. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 43).

Thesis (M.E.E.)--University of Delaware, 2006. / Principal faculty advisors: Kenneth E. Barner, Dept. of Electrical and Computer Engineering; and Mohsen Badiey, Dept. of Marine and Earth Studies. Includes bibliographical references.

Cheng Yuen Chung Armstrong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 146-150). / Acknowledgements --- p.i / Abstract --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- South China Orography --- p.2 / Chapter 1.2 --- Winter Monsoon Surges in Southern China --- p.3 / Chapter 1.3 --- Forecasts on the Northerly Surges and Effects on Local Weather --- p.5 / Chapter 1.4 --- Motivations and Objectives --- p.6 / Chapter 2 --- The Numerical Model --- p.8 / Chapter 2.1 --- Model Domain and Grid Structure --- p.10 / Chapter 2.1.1 --- Model domain --- p.10 / Chapter 2.1.2 --- Grid structure --- p.11 / Chapter 2.2 --- Governing Equations --- p.11 / Chapter 2.3 --- Finite Differencing Schemes and Lateral Boundaries --- p.14 / Chapter 2.3.1 --- Spatial differencing scheme --- p.15 / Chapter 2.3.2 --- Time integration scheme --- p.16 / Chapter 2.3.3 --- Choice of time step --- p.17 / Chapter 2.3.4 --- Lateral boundary conditions --- p.20 / Chapter 2.4 --- Development of Unevenly Spaced Vertical Levels --- p.20 / Chapter 2.4.1 --- Differencing scheme in vertical direction --- p.22 / Chapter 2.4.2 --- Integration of the hydrostatic equation --- p.26 / Chapter 2.4.3 --- Consideration of consistency in vertical and horizontal resolution --- p.27 / Chapter 2.5 --- Boundary Layer Physics --- p.30 / Chapter 2.5.1 --- Basic theory --- p.31 / Chapter 2.5.2 --- Turbulence closure --- p.33 / Chapter 2.5.3 --- Budget equation for turbulent kinetic energy --- p.36 / Chapter 2.5.4 --- Static and dynamic stability --- p.38 / Chapter 2.5.5 --- The logorithmic wind profile --- p.40 / Chapter 2.5.6 --- Bulk aerodynamics --- p.42 / Chapter 2.5.7 --- Boundary layer parameterization schemes of the model --- p.44 / Chapter 2.6 --- Parameterization of Precipitations --- p.48 / Chapter 3 --- Numerical Experiments --- p.53 / Chapter 3.1 --- Simulations From the Original Version --- p.54 / Chapter 3.2 --- Simulations From the Unevenly Spaced Version --- p.67 / Chapter 3.2.1 --- 10unevenly spaced levels simulation --- p.67 / Chapter 3.2.2 --- 17unevenly spaced levels simulation with enhanced PBL resolution --- p.71 / Chapter 3.3 --- Simulations With the Modified Boundary Layer Parameterization Schemes --- p.73 / Chapter 4 --- Case Studies of Northerly Cold Surges --- p.77 / Chapter 4.1 --- Lag-correlation Analysis --- p.78 / Chapter 4.2 --- Case Study I --- p.80 / Chapter 4.2.1 --- General descriptions --- p.81 / Chapter 4.2.2 --- Forecasts in ROHK --- p.84 / Chapter 4.2.3 --- 500hPa vorticity --- p.84 / Chapter 4.2.4 --- Numerical simulations --- p.87 / Chapter 4.3 --- Case Study II --- p.90 / Chapter 4.3.1 --- General descriptions --- p.90 / Chapter 4.3.2 --- Potential temperature advection --- p.90 / Chapter 5 --- A Forecast Index for Northerly Cold Surges --- p.99 / Chapter 5.1 --- The Internal Froude Number --- p.100 / Chapter 5.2 --- Case Investigations of a Critical Internal Froude Number over Nan Ling Ranges --- p.102 / Chapter 5.2.1 --- Case study I --- p.103 / Chapter 5.2.2 --- Case study II --- p.104 / Chapter 5.2.3 --- Case study on other events --- p.105 / Chapter 6 --- Conclusion --- p.112 / Appendices --- p.115 / Chapter A --- Computational Dispersion of Shallow Water Equation in f-Plane --- p.115 / Chapter B --- Rossby Radius in a Continuously Stratified Fluid --- p.119 / Chapter C --- Boussinesq Approximation of Navier-Stokes Equation --- p.123 / Chapter D --- Depth of the Neutral Boundary Layer --- p.125 / Chapter E --- Lag-correlation Analysis --- p.128 / Chapter F --- Fortran Source Code of the Numerical Model --- p.131 / Bibliography --- p.146