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
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/21685 |
| Date | January 2016 |
| Creators | Gray, Kevin Alan |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (vi, 72 leaves), application/pdf |
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