# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 5 Conclusions  
Amidst the worldwide transition to sustainable energy sources, wind power has become a crucial alternative to conventional fossil fuels. Wind farms, consisting of multiple turbines, play a key part in the production of sustainable energy. This article has conducted an in-depth investigation into the possibilities for transformation by combining IoT technology and machine learning in the wind energy industry. The research has demonstrated the capacity to gather crucial data from wind turbines by employing IoT devices in the wind energy industry, which is then converted into practical insights. The successful incorporation of weather data obtained from the IoT into the prediction of wind power generation has effectively connected meteorological observations with data on wind energy production. Moreover, the utilization of diverse machine learning methodologies can facilitate accurate prediction of energy generation. The comparison of Linear, Random Forest and Lasso Regression models provides insight into their distinctions, enhancing the understanding of their respective applications and identifying the strengths, weaknesses and suitability of each model. The assessment of each model's performance is derived from the comparison of R\({}^{2}\), Adjusted R\({}^{2}\), MSE, RMSE, MAE, and criteria AIC, BIC. The  
Figure 20: Comparison of regression models by criteria (AIC, BIC).  
results show that the random forest algorithm attains the highest R\({}^{2}\) and Adjusted R\({}^{2}\), along with the lowest MSE, RMSE, and MAE. The criteria display the lowest AIC and BIC values among the three models, indicating optimal data fit while maintaining a balance between complexity and performance. Future development should combine ML techniques with a cost-effective strategy that lowers the costs associated with IoT usage, fully integrating the significance of IoT.  
Conceptualization, C.E. and P.G.; methodology, C.E.; software, C.E.; validation, C.E. and P.G.; formal analysis, P.G.; investigation, C.E.; resources, C.E.; data curation, C.E.; writing--original draft preparation, C.E.; writing--review and editing, P.G.; visualization, C.E.; supervision, P.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
Adjusted R\({}^{2}\)Adjusted Coefficient of Determination  
AIC Akaike Information Criterion  
ANOVA Analysis of Variance  
ANNs Artificial Neural Networks  
AWGN Additive White Gaussian Noise  
BIC Bayesian Information Criterion  
CART Categorical Regression Tree  
CM Configuration Management  
CMS Content Management Systems  
CNNs Convolutional Neural Networks  
CPS Cyber-Physical System  
CRM Customer Resource Management  
ERP Enterprise Resource Planning  
IoE Internet of Everything  
IoT Internet of Things  
kN-Nearest Neighbors  
KPI Key Performance Indicator  
LCoE Levelized Cost of Energy  
LR Linear Regression  
LSTM Long Short-Term Memory  
MAE Mean Absolute Error  
M2M Machine to Machine  
ML Machine Learning  
MLR Multilinear Regression  
MSE Mean Squared Error  
R\({}^{2}\) Coefficient of Determination  
RFR Random Forest Regression  
RMSE Root Mean Squared Error  
RNNs Recurrent Neural Networks  
SCADA Supervisory Control and Data Acquisition  
VIF Variance Inflation Factor  
WECS Wind Energy Conversion System