# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 3 ML for Wind Energy Prediction
### Machine Learning and Wind Energy Forecast  
ML algorithms have the capability to identify alterations in the surroundings and adjust their actions accordingly. Regression analysis refers to this specific subset of categorization [26; 27]. The objective of this part is to structure the prediction in the wind energy domain. Following the technique or data analysis, ML is employed to predict the power energy output. This extensive dataset provides valuable insights on the correlations between various weather patterns and the generation of wind energy. By using predictive models and analyzing meteorological data, it is feasible to forecast power output. Regression models are utilized to determine the correlation between alterations in one or more explanatory variables and alterations in the dependent variable. To determine the regression model that exhibits the greatest efficiency and the lowest MSE, three regression models will be used and compared [28]. The AIC and BIC criteria will be part of the comparison and selection of the most appropriate model for this dataset, identifying accurately the underlying patterns while minimizing overfitting. The comparison of linear regression, random forest regression, and lasso regression will yield valuable results and provide an opportunity to gain a more comprehensive comprehension of each regression model's capabilities and weaknesses. Regression is extensively used in the field of big data to build predictive models. These models are designed to forecast certain outcomes for incoming data, rather than interpreting existing data. Regression analysis is a reliable method for identifying the variables that have an impact on a specific topic of interest [27]. Regression analysis allows for the accurate identification of the key aspects, the ones that may be ignored, and the correlations between these elements. * Dependent Variable: The dependent variable is the primary factor that one seeks to anticipate or comprehend. * Independent Variables: These variables are postulated to exert an influence on the dependent variable. The metrics included in the regression models are R2, Adjusted R2, MSE, RMSE, and MAE. These indices represent the goodness of fit, with the following definitions for each [29]:  
* R2 (Coefficient of Determination): Assesses the model's efficacy in elucidating the variance of the target variable. Varies from 0 to 1, with proximity to 1 indicating a superior fit [28]. * Adjusted R2: Analogous to R2, although modified to account for the quantity of predictors in the model. Addresses overfitting; increases solely if additional predictors enhance the model. * MSE: The mean of the squared deviations between expected and actual values. Imposes more penalties on larger faults compared to lesser ones. * RMSE: The square root of the MSE. Denotes the mean error in the identical units as the target variable. * MAE: The mean of the absolute discrepancies between expected and actual values. More robust to outliers than MSE or RMSE. The AIC and BIC are two commonly used criteria in statistical model selection and comparison. * AIC: Assesses a model's quality in comparison to other models, considering both the model's goodness-of-fit and its complexity. The AIC is computed as follows:  
\[\text{AIC}=2k-2\text{ln}(L) \tag{5}\]
* \(k\) is the number of parameters in the model;
* \(L\) is the maximum likelihood of the model. The term \(2k\) acts as a penalty for model complexity, reducing the chance of overfitting by favoring simpler models when they achieve a comparable fit. * BIC: Also evaluates model quality by imposing a more stringent penalty on the number of parameters, particularly as the sample size grows. The BIC is defined as follows:  
\[\text{BIC}=\text{ln}(n)k-2\text{ln}(L) \tag{6}\]  
where:  
* \(n\) is the number of observations;
* \(k\) is the number of parameters;
* \(L\) represents the likelihood function, which represents the probability of observing the given data under specific model parameters.