## 2 Literature Review
The proposed method achieved an RMSE of 7.5%. [PERSON] et al., 2019 [13] presented an SVM-based regression algorithm that was utilized to estimate wind energy output in Estonia one day in advance. The suggested method was afterwards compared with the prediction algorithm used by the Estonian energy regulating agency. Results reveal that the proposed approach offered more accurate forecasts and the lowest RMSE values. The research emphasized the application of an SVM-based regression method for predicting wind energy output, which can enhance supply and demand balance in power networks. Based on a high-frequency SCADA database with a 1-s sampling rate, [PERSON] and [PERSON] 2020 [14] developed a deep learning neural network to forecast wind power. On the basis of the physical process of offshore wind turbines, the linear and nonlinear correlations of the input characteristics were explored. Eleven factors, including wind speeds at various heights, pitch angles of each blade, average blade pitch angle, nacelle orientation, yaw error, and ambient temperature, were included in the prediction model. With an R2 of 0.91, the findings demonstrate that the suggested method may minimize computing cost and time while retaining high accuracy in wind power forecasting. [PERSON] et al., 2019 [15] applied five different ML algorithms to forecast wind power values. Specifically, Lasso regression, kNN regression, XGBoost regression, RF regression, and support vector regression (SVR) have been implemented and the dataset that they utilized comprises 5 years of hourly wind speed observation values from Nigde, Turkey. SVR and RF outperformed the other ML models by achieving an RMSE of 93.13 and 123.88, and an MAE of 32.63 and 42.76, respectively. Moreover, [PERSON] et al., 2019 [16] proposed a hybrid approach which combined principal component analysis (PCA) and deep learning to forecast wind power. PCA was used to extract hidden characteristics from wind data and eliminate strong correlations between values, while a TensorFlow-optimized deep learning system was employed to anticipate wind power based on relevant characteristics. The suggested technique was evaluated on three distinct datasets (hourly, monthly, and annually) from the National Renewable Energy Laboratory (NREL), and the findings were found to demonstrate that it successfully predicted wind output throughout a variety of time scales, from hours to years. Some of the existing applications of ML for wind power forecasts are based on big data analytics [17], the importance of atmospheric turbulence and stability [18], and interpretable physics [19]. On the other hand, evolutionary algorithms are a type of optimization technique based on the principles of natural selection. In the context of renewable energy production, evolutionary algorithms can be employed to improve ML models' accuracy or to determine the layout of a renewable energy system that maximizes production and minimizes costs. In addition, evolutionary algorithms can be used to optimize the functioning of renewable energy systems in real time, responding to changing weather conditions and other variables that might have an influence on the generation of renewable energy. [PERSON] et al., 2019 [20] presented a novel hybrid optimization algorithm for the optimal sizing of a stand-alone hybrid solar and wind energy system in remote and island areas where weather data are often unavailable. Their algorithm is based on three optimization techniques: chaotic search, harmony search, and simulated annealing. Weather forecasting data were utilized along with artificial neural networks to improve the accuracy of the results, and the objective function focused on minimizing the total life cycle cost while accounting for system reliability. Their results show that the proposed hybrid optimization algorithm with weather forecasting data could improve the efficiency and feasibility of a stand-alone hybrid renewable energy system for remote and island areas.