# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Point Forecasting Results
#### 3.4.2 Artificial Neural Network and Additive Quantile Regression Averaging  
This section presents the results of using the ANN and the main forecasts combination algorithm (AQRA) for point forecasting. ANN was implemented using the computational tool known as the Python 3 programming language. Following the steps from data curation to data explorations and visualisations along with data normalisation, this model is ready to carry out predictions from the variables selected by the LassoCV model. The ANN model was constructed from the SKLearn Library by importing MLPRegressor. The major determinants of an ANN are the number of layers in the network, the number of hidden layers and the number of nodes used for the hidden layer. ANN was constructed using three layers of input, hidden and output. The number of the hidden layers was made to be 1 by default, and the number of nodes in the hidden layer was given as 5. This makes the neural network thus constructed similar to the Bayesian Neural Network (BNN) in the work of [PERSON] [16]. In a BNN, the values of the \(\alpha\) and \(\beta\) are very crucial, which are between 0 and 1 for a  
Figure 4: Benchmark density and point forecasts plots.  
BNN. We thus performed hyper-parameter tuning to find the best \(\alpha\) values, best parameter and best accuracy making use of the GridSearchCV from the SKLearn model selector library.  
AQRA, on the other hand, is a model formed from combining point forecasts from the other models. This model was fitted with forecasts from the BNN and the benchmark models (GAM and SGB) as its predictor variables, while the response variable is the point forecasts of the AQRA model. It was implemented using \"ggam\" library developed by [PERSON] et al. [42] on the R computational tool. The AQRA model was set at a seed of 1000 and functions such as tune-learn-fast as its object containing the actual forecasts and the point forecasts from the other three models fitted at a quantile value of \(q=0.5\). Plots of the point forecasts from ANN and AQRA models are shown in Figure 5. Other necessary visualisations from these two models and the rest are shown in Appendix A (Figures A1, A3 and A4).  
In Figure 5, it is noticed that the highest wind speed forecast lie between 15 and 20 m/s and the highest dense plot for the wind speed is at a position very close to 5 m/s with a density of a little above 0.10. These two density plots also present these two models as viable models for wind speed forecasting because of the proximity of the actual and point forecasts values as represented by the two lines in the density plots. The density plot shows the densest point of the air in which an increasing wind speed gives a decline in its density until it returns to zero. The air density relates proportionally to the power realisable from the wind turbine [2; 49].  
The forecasts from these four models show the wind speed values lie in the range of 5-20-m/s. This corresponds to the linearly progressing and constant power region of the power curve of a wind turbine in [4]. This shows that both the actual values and the values from forecasts are within the range of values where electric power is being generated from the WT. A strong, increasing and constant  
Figure 5: ANN and AQRA density and point forecasts plots.  
electrical energy can thus be realised from the wind speed values, which depicts the richness of this renewable energy for generating electrical power.