Short term wind power forecasting in South Africa using neural networks

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dc.contributor.advisor Sigauke, Caston
dc.contributor.advisor Chibaya, Colin
dc.contributor.advisor Mbuvha, Rendani
dc.contributor.author Daniel, Lucky Oghenechodja
dc.date 2020
dc.date.accessioned 2020-10-05T07:42:02Z
dc.date.available 2020-10-05T07:42:02Z
dc.date.issued 2020-08-11
dc.identifier.citation Daniel, Lucky Oghenechodja (2020) Short term wind power forecasting in South Africa using neural networks. University of Venda, South Africa.<http://hdl.handle.net/11602/1591>.
dc.identifier.uri http://hdl.handle.net/11602/1591
dc.description MSc (Statistics) en_ZA
dc.description Department of Statistics
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_ZA
dc.description.sponsorship NRF en_ZA
dc.format.extent i online resource (xv, 95 leaves : color illustrations)
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject Additive quantile regression averaging en_ZA
dc.subject Forecasts combination en_ZA
dc.subject Machine learning en_ZA
dc.subject Point and interval forecasting en_ZA
dc.subject Renewable energy en_ZA
dc.subject Wind energy en_ZA
dc.subject.ddc 621.3121360968
dc.subject.lcsh Wind power -- South Africa
dc.subject.lcsh Power resources -- South Africa
dc.subject.lcsh Renewable energy resources -- South Africa
dc.subject.lcsh Wind energy conversion systems -- South Africa
dc.subject.lcsh Wind forecasting -- South Africa
dc.subject.lcsh Weather -- Forecasting
dc.title Short term wind power forecasting in South Africa using neural networks en_ZA
dc.type Dissertation en_ZA

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