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.
Highlights
Accurate wind speed prediction helps in maximising wind power generating facilities by reducing mistakes and economic cost involved in the planning and effective running of such facilities [6]
The results show that artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) performed better for the ultra-short-term, while the autoregressive integrated moving average models (ARIMA) model performed better for the short-term, 1 h ahead wind speed and wind power forecasting using root mean square error (RMSE)
The use of generalised additive models (GAMs) for other forms of forecasting but not for wind speed forecasting is the main gap filled by this work
Summary
The reduction in conventional energy sources, skyrocketing prices of fossil fuels, along with the attendant effect on environmental degradation and pollution from the emission of greenhouse gases (GHG), as well as global warming, necessitates the use of renewable energy sources [1,2]. Wind energy generated in the world has increased to a 250-GW cumulative wind power capacity as of 2012, and it is projected to increase to. Wind speed is the main wind information amongst others. Its predictability is essential for assessing wind energy exploitation purpose such as wind power generation. Accurate wind speed prediction helps in maximising wind power generating facilities by reducing mistakes and economic cost involved in the planning and effective running of such facilities [6]
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