# Prediction of Wind Power with Machine Learning Models
## 1 Introduction
Using multi-layered network structures and nonlinear optimisation techniques, DL models could automatically extract meaningful features from data at various levels of abstraction, from low-level to high-level representations [19]. Several scientists endeavour to employ DL models in wind power prediction using past data to enhance the precision of wind power forecasting [20; 21; 22]. Recently, there has been an extensive exploration into the realm of DL, focusing on its implementation in short-term wind power prediction [13]. LSTM and CNN are recognised as the two primary DL models [23]. Existing individual CNN and LSTM models can establish nonlinear correlations between output and input variables by utilising large amounts of historical data. This enables accurate predictions of wind speed or wind power [24]. These days, there has been notable progress in utilising DL algorithms for short-term wind power forecasting. Amongst the most distinct techniques employed is the LSTM network, which has proven to be highly effective [1]. LSTM networks can efficiently leverage the internal associations among time series data. However, they must achieve high prediction accuracy when dealing with discontinuous data features [25]. Among the diverse deep neural networks (DNNs), CNNs stand out for their seamless processing of multi-dimensional data samples. This distinctive attribute enables CNNs to extract intrinsic features from the data effectively. Furthermore, the weight-sharing structure inherent to CNNs reduces the number of parameters, resulting in decreased network complexity and effectively addressing the concern of overfitting [26; 27]. The literature encompasses numerous studies on wind power estimation utilising CNN and LSTM models. This research aimed to estimate the power generation of the wind power plant using ML techniques, namely, ANN, RNN, CNN, and LSTM networks. This study combines two independent data sets to predict wind power accurately. The first data set contains internal values such as theoretical power (kW) and active power (kW) of the wind turbine. This first data set was taken from the Esenkoy wind power plant SCADA system. The second data set is external values that contain the meteorological data set, which can affect the wind power forecast. The second data set was obtained from MERRA-2 Global. Subsequently, the prediction performances generated by these methods were evaluated and compared using a variety of metrics [28,29]. The current investigation has achieved significant progress, which can be summarised in four key points. 1. In the current investigation, a series of multi-objective predictive models were created utilising a range of cutting-edge ML methodologies, such as CNN and LSTM, to augment the precision of prognostication. 2. Additional input parameters have been incorporated with wind speed, wind direction, active power, and theoretical power data obtained via the SCADA system to enhance the models' predictive capabilities. These supplementary parameters encompass a range of weather-related factors, such as air temperature, precipitation, and air density. 3. The current investigation incorporates statistical performance deviation indicators to substantially augment the precision of prognostications and effectively demonstrate the efficacy of the employed methodology. 4. The current investigation entails meticulously analysing methodologies' most favourable parameter parameters through input-output correlation matrices. Consequently, the degree to which the independent variables influence the dependent variable is established. The following sections of this manuscript are structured as follows. In Section 2, we offer insights into data sources, data preprocessing, error metrics, and the fundamental methodology. In Section 3, we present the wind power estimation results, including R2 values and error metrics for the proposed methods.