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Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

1
Department of Statistics, University of Venda Private Bag X5050, Thohoyandou 0950, South Africa
2
DST-CSIR National e-Science Postgraduate Teaching and Training Platform (NEPTTP), LG07, Mathematical Sciences Building, West Campus, Private Bag 3, Wits 2050, Gauteng, South Africa
3
Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley 8300, South Africa
4
Department of Statistics, University of Witwatersrand, Private Bag 3, Wits 2050, Gauteng, South Africa
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 23 March 2020 / Revised: 17 May 2020 / Accepted: 19 May 2020 / Published: 26 May 2020
Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance. View Full-Text
Keywords: additive quantile regression averaging; forecasts combination; machine learning; point and interval forecasting; renewable energy; wind energy additive quantile regression averaging; forecasts combination; machine learning; point and interval forecasting; renewable energy; wind energy
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MDPI and ACS Style

Daniel, L.O.; Sigauke, C.; Chibaya, C.; Mbuvha, R. Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods. Algorithms 2020, 13, 132. https://0-doi-org.brum.beds.ac.uk/10.3390/a13060132

AMA Style

Daniel LO, Sigauke C, Chibaya C, Mbuvha R. Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods. Algorithms. 2020; 13(6):132. https://0-doi-org.brum.beds.ac.uk/10.3390/a13060132

Chicago/Turabian Style

Daniel, Lucky O., Caston Sigauke, Colin Chibaya, and Rendani Mbuvha. 2020. "Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods" Algorithms 13, no. 6: 132. https://0-doi-org.brum.beds.ac.uk/10.3390/a13060132

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