## 1 Introduction  
Due to limitations associated with conventional energy use and increasing environmental issues, wind energy is widely implemented because it represents a source of green renewable energy [1; 2]. The main use of wind energy is power generation, converting wind energy into electricity. However, due to the complex nature of the Earth's atmosphere, the instability of wind energy results in volatility and intermittency of the output power; this creates challenges for large-scale wind power projects and the planning and construction of power grids. Accurate wind power forecasting is one of the most effective measures to meet these challenges [3]. In recent years, many scholars have conducted research on wind power forecasting methods focused on three categories [4]--physical methods, statistical methods, and hybrid methods. Physical methods rely on numerical weather forecasting information and physical information, such as landforms near the wind turbines, to establish a mathematical forecasting model. The calculation process is cumbersome and, therefore, not convenient for real-time forecasting in wind farms [5; 6]. Statistical methods are usually based on historical data of wind speed and power, and establish a forecasting model to fit a nonlinear relationship. Statistical methods can be further subdivided into time series methods and machine learning methods [7]. In [8], a linear and nonlinear autoregressive moving average model (ARMA) was established to forecast wind speed; the model provided good forecasting performance in terms of mean absolute error, root mean square error, and mean absolute percentage error. A hybrid model based on the autoregressive integral moving average (ARIMA) was proposed in [9] to achieve ultra-short-term, short-term, medium-term, and long-term forecasting of wind speed. In [10], an autoregressive moving average (ARMAX) model was established to forecast wind power, wind speed, and wind direction, using exogenous variables and a threshold autoregressive method to deal with the intermittency of wind power. As a result of the rapid development of artificial intelligence, some scholars have used artificial neural networks, extreme learning machines, support vector machines, and fuzzy logic systems for wind power forecasting. In [11], a nonlinear autoregressive neural network model was established to forecast multi-step wind speed using direct and recursive strategies. A cross-optimization algorithm was used in [12] to train an extreme learning machine after the second decomposition of a wind power time series for wind power forecasting. Compared to similar models, the proposed method had several advantages. Data mining was used in [13] to correct the original data; a kernel function was optimized and a penalty factor was used in the support vector machine using a cuckoo algorithm to improve the forecasting accuracy of the wind power. In [14], a fuzzy neural network wind power forecasting model based on particle swarm optimization (PSO) was proposed, and fuzzy expert knowledge and neural network learning methods were combined. The hybrid method combines two or more forecasting methods to minimize forecasting errors and improve the reliability of wind power forecasting. In [15], a support vector machine, extreme learning machine, a bat back-propagation neural network (BPNN), and an Elman neural network are combined with dynamic weights to minimize the disadvantages and retain the advantages of the individual networks to improve forecasting accuracy. In [16], a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions, and four models were proposed and successfully applied to forecasting wind speed. Although the aforementioned forecasting methods have improved the accuracy of wind power forecasting to varying degrees, they are all shallow learning models, regardless of whether they are time series methods, machine learning methods, or hybrid methods.