# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 1 Introduction  
The depletion of traditional fossil fuels and the resulting pollution are making energy supply and improvement increasingly severe. Currently, wind power as a non-polluting and sustainable new energy has received widespread attention [1]. Renewable energy, represented by wind power, holds immense significance in addressing energy depletion and improving the energy structure. However, wind power is subject to variability and intermittency, which poses a challenge to grid stability when large-scale wind power integration takes place [2]. Accurate wind power forecasting is one such crucial solution that enhances the reliability and efficiency of power systems [3, 4, 5]. At the same time, it provides guidance for grid scheduling plans and maintains balance in wind power supply and demand [6, 7]. Wind power forecasting can be categorized into ultra-short-term, short-term, medium-term, and long-term forecasts based on the time horizon [8]. The ultra-short-term range is a few minutes (0-30 min), which can aid power system operators in real-time scheduling and the optimization of generation [9]. The short-term prediction range is from hours to days (hours-days), mainly used for market trading and the optimization scheduling of wind farms [10]. The medium and long-term predictions range from days to weeks, which can provide assistance and guidance for long-term maintenance planning and the energy management of wind farms [11]. Considering the stability and the economy of the power system, short-term wind power forecasting has become the focus of research. Physical models, statistical models, artificial intelligence models, and composite models are four commonly used methods [12]. Numerical weather prediction (NWP) is one of the most widely used physical models. It is based on physical equations and utilizes information about the surrounding physical environment to establish a prediction model [13]. This type of method does not require historical wind power data but is computationally complex [14]. Statistical models predict future wind power output by analyzing patterns and trends in historical data. The advantage of statistical models is their low computational cost. Traditional statistical models include the autoregressive moving average (ARMA) model [15] and the autoregressive integrated moving average (ARIMA) model [16]. The above statistical model assumes that the relationship between time series data is linear in advance, which cannot deal with the nonlinear characteristics of wind power series [17]. Artificial intelligence models exhibit remarkable nonlinear fitting capabilities when processing data and have been widely used in wind power prediction [18]. For example, artificial neural networks (ANNs) [19] and support vector machines (SVMs) [20] combined with optimization algorithms have been applied to short-term wind power forecasting, and these methods have been shown to exhibit excellent predictive performance. Deep learning, as a class of artificial intelligence methods, can learn more complex non-linear relationships, thus being highly favored in wind power forecasting [21]. For instance, recurrent neural networks (RNNs) can effectively process sequential data and produce good predictive results. However, considering that RNNs often faces the problems of vanishing or exploding gradients, variants of RNNs are mainly used, such as long short-term memory (LSTM) networks [22] or gated recurrent unit (GRU) networks [23]. Convolutional Neural Networks (CNNs) leverage unique convolution operations to effectively extract high-level features from wind power time series data, leading to accurate prediction results [24]. Nevertheless, these prediction models still have limitations. LSTM can only extract forward time information from the input and ignore backward time information [25]. [PERSON] et al. [26] proposed BiLSTM, a model that can simultaneously consider bidirectional information and achieves better predictive accuracy than LSTM.