## 1 Introduction  
With the increasing global warming threats, the United Nations has called for the reduction of carbon dioxide emissions and hence set out the goals of reducing greenhouse gas emissions by 45 percent by 2030 and to net zero emissions by 2050 [1]. In line with the United Nations' goals, the developed and most developing countries have started to take actions to develop realistic plans toward the reduction of carbon dioxide emissions. For instance, the Chinese government announced their goals to reach the peak of carbon dioxide emissions before 2030 and strive to achieve carbon neutrality before 2060 at the 75 th session of the United Nations General Assembly (UNGA 75) in September 2020 [2]. In 2019, the total carbon dioxide emissions in China were estimated at 10.5 billion tons, of which the carbon emissions from energy consumption were about 9.8 billion tons, accounting for around 87% of the total emissions [3]. With rapid economic and social development, the transition to a green and low-carbon society is accelerated, and the transition of the country's energy structure brooks no delay. Cleanliness is an important direction for carbon emission reduction in energy production. The way to accomplish the \"dual carbon\" task is to develop green and low-carbon renewable energy. During the 14 th Five-Year Plan period, coal consumption in China will continue to decline. In the plan, it is forecasted that the installed capacity of renewable energy and nuclear power will reach 1200 GW by 2030, of which wind power will reach 500 GW in China [4]. As the installed capacity of wind power continues to grow and wind power is connected to the grid on a large scale, the overall grid performance is affected by the output power from wind farms due to the intermittency of wind [5]. In order to ensure the safety and stability of the operation of the power system, the power grid needs to prepare a sufficient spinning reserve capacity. However, the increase in the reserve capacity will increase the operating cost of wind power. Therefore, accurate wind power forecasting (WPF) is required for providing a basis to develop a grid dispatch schedule, and it also helps to greatly reduce the operating costs of wind farms and improve the competitiveness of wind energy in the overall energy market [6; 7; 8]. Historically, there have been different wind power forecasting (WPF) methods, which can be divided into four categories: physical, statistical, hybrid, and deep-learning methods. A summary report of these four categories of methods in terms of their features and limitations in application is given in [9]. The physical method is based on a mesoscale weather model or a numerical weather prediction system (NWP). NWP represents a variety of mathematical expression models of geographic and meteorological information [10; 11]. Although this method has a good effect on short or medium-term forecasts of more than 3 h, it is difficult for it to collect all relevant geographic or meteorological data [12; 13; 14], so it has limitations in application. The physical forecasting method is generally used to select/determine new wind farms, but not for wind turbine power production prediction. The statistical prediction method is based on the historical data collected by the SCADA system to establish a linear/non-linear relationship between relevant index data and power to predict the output power of a wind farm. Statistical prediction methods can be categorized as conventional statistical methods and those based on artificial neural networks (ANN). The conventional statistical methods have limitations in forecasting due to the demand for non-linear expression in wind power forecasting (WPF), while the methods based on artificial neural networks can effectively represent a large number of non-linear relationships and complex characteristics among wind speed, temperature, and other parameters in power generation. Therefore, statistical prediction methods based on ANN have become widely applied. [PERSON].