# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 4 Discussion  
This study was geared towards investigating methods useful for forecasting short-term unpredictable and intermittent wind speed for power generation. The method with smaller errors indicates the most accurate and desirable method. Two approaches were investigated: individual forecasting models and forecast combination models. Amongst the individual models, the Bayesian NN for the ANN was compared with two benchmark models involving SGB (ML method) and GAM (SL method). The evaluation of these three methods presented ANN as the best using RMSE while SGB is the best from MAE and MAPE forecast accuracy measures. On the other hand, two forecast combination methods, both of which are statistical methods, were investigated for the forecast combination and AQRA performed better than LQRA. It further outperformed the individual forecasts containing three methods (ANN and the two benchmark models) using all three forecast accuracy metrics involving RMSE, MAE and MAPE. Therefore, the authors present AQRA from forecasts combination as the best method for point forecasting of wind speed having lesser error values and the most accurate model.  
Moving on to the prediction interval uncertainty measure, using only AQRA, the first uncertainty measure was the prediction interval width. Using this metric, our analysis at 95% confidence level shows that the model AQRA was the most symmetrical and the best model followed by SGB, even though ANN and GAM presented narrower prediction intervals than the others. None of these models gave a normal distribution and were all platykurtic. The research investigated further on interval forecasts, checking prediction intervals against the target values. Three confidence levels were considered: 90%, 95% and 99%. All the interval forecasts were checked for their validity, reliability, quality and degree of deviation from the actual values. All the models were valid models since PICP values were greater than the predetermined confidence levels. AQRA presented more reliable prediction intervals at 90%, having the highest coverage value. In addition, this constructed prediction interval at this level of confidence was deemed accurate and having satisfactory performance and quality since the lowest PINAW and PICAW values were recorded by AQRA followed by ANN. However, PINAD value shows ANN as the model with the least deviation. Actual values below and above the lower and upper limit kept decreasing, showing AQRA with the least values.  
Forecasts from the SGB model have the highest coverage at the 95% confidence level followed by GAM. Using PINAW and PICAW for quality, accurate and satisfactory performance evaluation presented AQRA as the best model, while ANN and AQRA have the least degree of deviation. Constant reduction was noticed with the actual values below and above the lower and upper limits, respectively. The 99% confidence level shows SGB as the most reliable from its highest PICP value followed by GAM while the quality, accurate and satisfactory performance PI was seen to be AQRA from its PINAW and PICAW values. The least deviation was noticed in ANN followed by AQRA. The actual values below and above the lower and upper limit showed no consistency at this confidence level. The best model is AQRA followed by SGB. Analysis from the interval combination method is also reported. We used the simple average method and the median method. The simple average gave an invalid PICP at the 99% confidence level. The median method outperformed the simple average method in all prediction interval (accuracy measurement) metrics considered and, thus, the best interval combination method. The residual analysis also presents AQRA as the best, having the narrowest error non-normal distribution along with the other models.