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Short-term wind speed forecasting using statistical and machine learning methods

Show simple item record Daniel, Lucky Oghenechodja Sigauke, Caston Chibaya, Colin Mbuvha, Rendani 2021-09-13T09:35:12Z 2021-09-13T09:35:12Z 2020
dc.identifier.issn 1999-4893
dc.identifier.uri 10.3390/a13060132
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher Algorithms en_US
dc.relation.ispartofseries volume 13;No 6
dc.subject additive quantile regression averaging en_US
dc.subject forecasts combination en_US
dc.subject machine learning en_US
dc.subject point and interval forecasting en_US
dc.subject renewable energy en_US
dc.subject wind energy en_US
dc.title Short-term wind speed forecasting using statistical and machine learning methods en_US
dc.type Article en_US

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