# Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
## 4 Conclusions  
In this work, machine learning-based models were used to predict the power and load outputs of a micro-scale wind turbine based on the blade's design parameters and inflow wind velocity. Data have been collected from extensive simulations on the QBlade wind turbine simulation tool, performing parametric analyses by changing different design parameters one at a time.  
The collected dataset has been processed and cleaned, and the feature selection process led to the choice of the input parameters for all models. The dataset is then split into training and testing sets for further modeling by machine learning algorithms.  
It was found that ensemble models gave the best accuracy for all the desired outputs. For almost all outputs, the Random Forest and Decision Trees Regressor came in first and second places, with accuracies of over 98% in prediction. Adaptive and gradient boosting models gave satisfactory results as well.  
Other models, like the linear regression model, could give sufficient results for preliminary predictions of normal and tangential forces on the blade. However, it should be used with caution since the accuracy is not high and could lead to inaccurate results that underestimate or overestimate the aerodynamic loads. The formulae for calculating the normal and tangential loads based on a first-order equation in the input features were given. The linear regression model was insufficient for power-related outputs, namely power coefficient and rotor torque. Hence, they are not recommended.  
Neural networks in their preliminary modeling could give sufficient results in predicting the blade and rotor loads, which encourages further investigations using hyperparameter tuning by changing the number of layers and the activation functions.  
The highest accuracy among all models was the Random Forest Regressor which could predict the power coefficient, tangential force, and rotor torque with accuracies of 99.9%, 98.4%, and 98.6%, respectively. While for normal force, the Decision Tree Regressor was of the highest accuracy with a value of 98.4%.  
The outcomes of this work could be further used to provide a preliminary design of a micro-scale wind turbine while optimizing the desired outputs. It could also be used to create a data-based wind turbine calculations tool to ease the wind turbine design process. Further investigations could include different output measures, different design parameters or operational conditions, or refine the already used parameters for better inclusion of all cases. The result of this work is limited to micro-scale turbines. For large utility-scale turbines, the data should be trained for such sizes and working conditions.  
Conceptualization, A.I.; methodology, A.E. and A.I.; simulations, R.M., S.O., H.A., M.N. and N.M.; validation, R.M., S.O., H.A., M.N. and N.M.; data acquisition: R.M., S.O, H.A., M.N. and N.M.; data analysis, All authors; writing--original draft preparation, R.M., S.O., H.A., M.N. and N.M.; writing--review and editing, A.E. and A.I.; visualization, A.I.; supervision, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.