dc.contributor.author |
Botha, Gerda N
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dc.contributor.author |
van der Walt, Christiaan
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dc.date.accessioned |
2018-08-22T13:10:13Z |
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dc.date.available |
2018-08-22T13:10:13Z |
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dc.date.issued |
2017-12 |
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dc.identifier.citation |
Botha, G.N. and van der Walt, C. 2017. Forecasting wind speed using support vector regression and feature selection. Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 2017, Bloemfontein, South Africa, 30 Nov -1 Dec 2017, pp. 181-186 |
en_US |
dc.identifier.isbn |
978-1-5386-2315-2 |
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dc.identifier.uri |
DOI: 10.1109/RoboMech.2017.8261144
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dc.identifier.uri |
http://ieeexplore.ieee.org/document/8261144/
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dc.identifier.uri |
http://hdl.handle.net/10204/10383
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dc.description |
© 2017 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the published item, please consult the publisher's website. |
en_US |
dc.description.abstract |
Since the wind power generated by a wind farm is entirely dependent on meteorological conditions (such as wind speed, wind direction, humidity etc.), accurately forecasting wind speed based on these conditions over a 1 to 24 hour time horizon is crucial to predict the potential short term energy supply of a wind farm. These short term predictions in turn are crucial in assisting with wind farm planning so that the required base load provided to the electricity grid is always guaranteed (even in the presence of highly variable wind power outputs). In this work we show that the relative prediction performance of a short-term Support Vector Regression (SVR) wind forecasting system can be improved by up to 11.12% by systematically selecting and combining relevant input features that influence short term wind speed. We illustrate our results on meteorological data collected in Alexander Bay, South Africa over a three year period from 2011-2013. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;19956 |
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dc.subject |
Forecasting |
en_US |
dc.subject |
Support vector machines |
en_US |
dc.subject |
Training data |
en_US |
dc.subject |
Wind Forecasting |
en_US |
dc.subject |
Wind speed |
en_US |
dc.title |
Forecasting wind speed using support vector regression and feature selection |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Botha, G. N., & van der Walt, C. (2017). Forecasting wind speed using support vector regression and feature selection. IEEE. http://hdl.handle.net/10204/10383 |
en_ZA |
dc.identifier.chicagocitation |
Botha, Gerda N, and Christiaan van der Walt. "Forecasting wind speed using support vector regression and feature selection." (2017): http://hdl.handle.net/10204/10383 |
en_ZA |
dc.identifier.vancouvercitation |
Botha GN, van der Walt C, Forecasting wind speed using support vector regression and feature selection; IEEE; 2017. http://hdl.handle.net/10204/10383 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Botha, Gerda N
AU - van der Walt, Christiaan
AB - Since the wind power generated by a wind farm is entirely dependent on meteorological conditions (such as wind speed, wind direction, humidity etc.), accurately forecasting wind speed based on these conditions over a 1 to 24 hour time horizon is crucial to predict the potential short term energy supply of a wind farm. These short term predictions in turn are crucial in assisting with wind farm planning so that the required base load provided to the electricity grid is always guaranteed (even in the presence of highly variable wind power outputs). In this work we show that the relative prediction performance of a short-term Support Vector Regression (SVR) wind forecasting system can be improved by up to 11.12% by systematically selecting and combining relevant input features that influence short term wind speed. We illustrate our results on meteorological data collected in Alexander Bay, South Africa over a three year period from 2011-2013.
DA - 2017-12
DB - ResearchSpace
DP - CSIR
KW - Forecasting
KW - Support vector machines
KW - Training data
KW - Wind Forecasting
KW - Wind speed
LK - https://researchspace.csir.co.za
PY - 2017
SM - 978-1-5386-2315-2
T1 - Forecasting wind speed using support vector regression and feature selection
TI - Forecasting wind speed using support vector regression and feature selection
UR - http://hdl.handle.net/10204/10383
ER -
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en_ZA |