## 1 Introduction  
With the rise of globalization, renewable and alternative energy sources, which address security issues associated with conventional energy sources, are increasingly being favored to provide power for a wide range of social and economic activities. Wind energy, a promising technology utilized in many renewable energy systems, has attracted an increasing amount of attention in recent decades due to the drive to meet the rapidly growing electricity demand across the globe without emitting environmental pollutants, such as CO\({}_{2}\). According to the _GLOBAL WIND REPORT 2021_[1], China possesses 39% (278324 MW) of the world's total onshore wind power capacity, and accounted for 56% of new onshore installations by the end of 2020, making it the world leader. As a result, the accurate forecasting of wind speed--the determining factor in wind energy electricity generation--has increasingly become a focus of public conversation, especially on the short-term horizon. Unfortunately, it is difficult to obtain excellent forecasting results because of the stochastic and nonlinear oscillations caused by uneven atmosphere stratification and complex topography [2]. In recent years, the capacity of onshore wind power sets has been rapidly increasing, which requires high reliability and maintainability in wind turbines with poor natural conditions. Various methods have been developed to analyze wind turbine rotor blades. To identify the best-performing design for blade geometry, aerodynamic analyses are used to specify the desired radial distributions of the angle of attack, \(\alpha\) (or sectional lift coefficient, cl) and axial induction factor along the blade and iterate the blade geometry (determined by the radial distributions of the blade chord, \(c\), and twist, \(\theta\)) until the required specifications are met. In aerodynamic evaluated processing, [PERSON] and [PERSON] [3] use the blade element momentum (BEM) theory as for a method for aerodynamic analysis. [PERSON] [4] used the vortex line method (VLM) and [PERSON] and [PERSON] [5] resolved Reynolds-averaged Navier-Stokes (RANS) equations using an actuator disk model (ADM) to emulate the rotor as a body force. These extensions permit analysis of unconventional rotor- and blade geometries, e.g., multi-rotor turbine configurations and rotor blades with dihedral and/or sweep. Meanwhile, the blade design of the wind turbine also affects the turbine output power coefficient (CP). For example, [PERSON] et al. [6] restricted the chord and twist distributions to linear profiles to increase the manufacturability of their design, and designed the rotor blade geometry (chord and twist) of a 1 MW wind turbine to maximize the CP. [PERSON] et al. [7] investigated the blade design of a fixed-pitch, fixed-speed (FPFS) horizontal-axis wind turbine to maximize its annual energy production (AEP) for a prescribed wind speed Weibull distribution. They held the blade geometry fixed at the blade tip and assumed linear variations to reduce the design variables of the chord and twist values at the blade root. Motivated by the need to urgently develop more precise measurement methods, a variety of studies have been conducted on wind speed forecasting over the past few decades. These studies can be broadly divided into two types: causal analyses and time series-based analyses [8]. Causal analyses use the established historical causality between explanatory and interpreted variables to forecast changes in future dependent variables [9]; however, such methods are prone to multiple collinear negative effects. Time series-based analyses can be subdivided into four approaches: physical [10], statistical [11], artificial intelligence [12], and hybrid [13] or combined models [14]. Advantages and shortcomings of those models include the following:  
1.