# Prediction of Wind Power with Machine Learning Models
## 1 Introduction  
Within the realm of renewable energy, wind power has emerged as a prominent contender, primarily due to its sustainable nature, lack of pollution, and minimal cost implications. However, the randomness of wind power generation challenges the power grid's secure dispatch and stable operation. Hence, precise wind power forecasting significantly reduces grid dispatching costs and enhances system performance [1, 2]. Various factors, including climate, seasons, and the intermittent nature of wind, make forecasting wind power complex [3]. Furthermore, the lack of predictive abilities in wind power systems that undergo substantial fluctuations may result in contradictions and pose significant obstacles for power systems. Therefore, the successful integration of wind power at a global level relies heavily on accurate wind power prediction. It is demonstrated that challenges such as insufficient regulation and reserve power, often linked to the variability and limited predictability of wind power, can only be comprehensively evaluated when considering the characteristics of the conventional generation system with which wind power is integrated [4, 5]. Scientists worldwide have conducted numerous research efforts to realise wind power prediction by developing various models in recent years. Numerous researchers, e.g., [PERSON] et al. [6], [PERSON] et al. [7], and [PERSON] et al. [8], have undertaken significant investigations to develop refined software models that are intended to predict power generationvia the utilisation of RES. Some researchers have found that the amount of variable data affects wind power forecasting performance [9; 10]. [PERSON] and [PERSON] [11] proposed a method that utilises two-day historical climate data and wind power data for training a back propagation neural network. This network is employed to predict ultra-short-term wind power in the next 4 h based on numerical weather forecasts. [PERSON] et al. [12] discovered in their study on short-term generation forecasting in a microgrid that neural networks consistently exhibit high performance across seasons. Unlike other algorithms, this method is not influenced by temperature variations, showcasing its remarkable flexibility. [PERSON] et al. [13] studied hour-ahead wind power forecasting. They observed that neural networks are sensitive to irrelevant data, with model performance decreasing as the number of features increases. Additionally, their research revealed that the performance of neural networks can be compromised if their numerous parameters are not adequately tuned. In a study referenced in [14], a probability forecasting model for ultra short-term wind power was developed using a CNN. The accuracy of this model was subsequently evaluated to assess its performance. In [5], CNN and a physical model were integrated to enhance the accuracy of short-term wind power forecasting, significantly reducing forecasting errors. In [15], LSTM models have been utilised in short-term wind speed and power forecasting. [PERSON] et al. [16] put forward a concise approach for wind power prediction, which relies on a CNN. Their findings revealed that this method outperforms ARIMA and Gradient Supercharger regarding wind power forecasting accuracy. [PERSON] and colleagues [17] posited an innovative methodology for short-term wind power forecasting that leverages image representations of temporal data and applies CNN architectures for analysis. Comparative analyses with current approaches in wind power prediction, namely, RNN, LSTM, and GRU, exhibit the superior performance of the proposed method. ANN-based forecasting enables rapid wind farm output power prediction despite the potential for significant output power disparities amongst individual wind generators resulting from inconsistencies in wind speed at each turbine [18]. Deep learning (DL) models offer a more robust computational capability and are better equipped to handle complex functions than shallow ML approaches.