Botha, NicoleneVan der Walt, Christiaan2017-07-282017-07-282016-12Botha, N. and Van der Walt, C.M. 2016. A comparison of regression algorithms for wind speed forecasting at Alexander Bay. Proceedings of the Twenty-Seventh Annual Symposium of the Pattern Recognition Association of South Africa, 30 November - 2 December 2016, Stellenbosch, South Africa. doi: 10.1109/RoboMech.2016.7813147978-1-5090-3335-5doi: 10.1109/RoboMech.2016.7813147http://ieeexplore.ieee.org/document/7813147/http://hdl.handle.net/10204/9368Copyright: 2016 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website.With the drive to reduce carbon emissions, the use of renewable energy such as wind power, solar power, hydropower and biofuel has become more prevalent globally. In the case of wind farms, the power generated by wind turbines is highly correlated to wind speed and direction. As a consequence, considerable research is currently being performed to accurately predict wind speed and direction ahead of time. In this paper the wind speed of South African data from the Wind Atlas of South Africa is used to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting prediction errors from each method are compared to persistence forecast which serves as a performance benchmark. The results show a vast improvement on the persistence forecast and a slight improvement of the support vector regression over the ordinary least squares and Bayesian ridge regression. We also show that there is an additional improvement in prediction error when more features are added.enWind speed forecastingOrdinary least squares regressionBayesian ridge regressionSupport vector regressionSupport vector machinesConference PresentationBotha, N., & Van der Walt, C. (2016). A comparison of regression algorithms for wind speed forecasting at Alexander Bay. IEEE. http://hdl.handle.net/10204/9368Botha, Nicolene, and Christiaan Van der Walt. "A comparison of regression algorithms for wind speed forecasting at Alexander Bay." (2016): http://hdl.handle.net/10204/9368Botha N, Van der Walt C, A comparison of regression algorithms for wind speed forecasting at Alexander Bay; IEEE; 2016. http://hdl.handle.net/10204/9368 .TY - Conference Presentation AU - Botha, Nicolene AU - Van der Walt, Christiaan AB - With the drive to reduce carbon emissions, the use of renewable energy such as wind power, solar power, hydropower and biofuel has become more prevalent globally. In the case of wind farms, the power generated by wind turbines is highly correlated to wind speed and direction. As a consequence, considerable research is currently being performed to accurately predict wind speed and direction ahead of time. In this paper the wind speed of South African data from the Wind Atlas of South Africa is used to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting prediction errors from each method are compared to persistence forecast which serves as a performance benchmark. The results show a vast improvement on the persistence forecast and a slight improvement of the support vector regression over the ordinary least squares and Bayesian ridge regression. We also show that there is an additional improvement in prediction error when more features are added. DA - 2016-12 DB - ResearchSpace DP - CSIR KW - Wind speed forecasting KW - Ordinary least squares regression KW - Bayesian ridge regression KW - Support vector regression KW - Support vector machines LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-5090-3335-5 T1 - A comparison of regression algorithms for wind speed forecasting at Alexander Bay TI - A comparison of regression algorithms for wind speed forecasting at Alexander Bay UR - http://hdl.handle.net/10204/9368 ER -