Gray, Kevin Alan2017-01-192017-01-192016Gray, Kevin Alan (2016) Virtual wind sensors: improving wind forecasting using big data analytics, University of Witwatersrand, Johannesburg, <http://wiredspace.wits.ac.za/handle/10539/21685>http://hdl.handle.net/10539/21685A 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.Online resource (vi, 72 leaves)enWind forecastingBig dataThesis