# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 1 Introduction
The numerical findings indicate that the suggested technique can accurately predict the condition of a wind turbine and greatly surpasses previous estimating methods. In [6], a set of multi-objective predictive models was developed employing various advanced ML algorithms, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. According to the outcomes of this work, the LSTM, RNN, CNN, and ANN algorithms were effective in predicting wind power. The efficacy of these models was assessed by the integration of statistical metrics for performance deviation. In conclusion, the LSTM model is more effective in predicting wind power. In [7], long-term wind power forecasting was conducted utilizing daily wind speed data through five machine learning algorithms: LASSO regression, k-nearest neighbors (kNN) regression, xGBoost regression, random forest regression (RFR), and support vector regression (SVR). The findings of this work indicated that ML algorithms are capable of predicting long-term wind power values based on previous wind speed data and can be utilized in sites distinct from those used for model training. Among these algorithms, RFR had improved performance compared to the other approaches, while LASSO had the worst performance metrics due to its linear basis. In [8], wind speed and solar radiation are the fundamental inputs used in the context of renewable energy sources. To this end, the performance of various artificial neural networks algorithms has taken place to ensure high-quality predictions. These algorithms can accurately predict data like temperature, relative moisture, solar radiation, rain, and wind speed. Overall, the results show that the Levenberg-Marquardt and Bayesian algorithms for regulation are better at predicting properties that are not linear. Components, such as sensors, actuators, protocols, and gateways, enhance energy efficiency, reliability, failure detection, and production optimization. After gathering precise data, IoT emerges as the superior platform for real-time data analysis. In [9], through the analysis of climate change and ground temperature data, Random Forest (RF), LSTM, and XGBoost models were utilised to predict the artificial permafrost table, while grid search and cross-validation techniques were applied to optimise the hyperparameters of each model. Thereafter, the significance of the variables in the ML model was assessed. The results demonstrated a robust association with alterations in the artificial permafrost table. With mean squared error (MSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), and R\({}^{2}\) values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, the integrated model was better at making predictions than the individual models. This model provides a swift, precise, and dependable method for predicting the permafrost table and enhancing subgrade stability research under difficult permafrost conditions. In [10], the operational complexities indicate that traditional methods are inadequate, and emerging \"big data\" techniques, particularly ML and deep learning, are set to significantly influence the design and optimization of offshore wind turbines and farms. In all the aforementioned studies, the researchers concentrate their efforts either on IoT in wind energy or in wind power prediction. Therefore, a key novel point of this work is the integration of IoT technology with wind energy systems, as well as the comparison of three fundamental ML algorithms to extract valuable information about their strengths and weaknesses. The model evaluation indicators (R\({}^{2}\), adjusted R\({}^{2}\), MSE, RMSE, and MAE) are employed to evaluate the performances of the ML models as well as the Akaike information criterion (AIC) and the Bayesian Information Criterion (BIC) criteria for model selection and comparison [11].