## 1 Introduction  
Renewable energy has been a popular choice for power generation and has become a crucial part in government development schemes. Wind energy is a kind of clean and pollution-free renewable energy source and its reserves are huge. By 2008, wind power production was about 94.1 million kilowatts globally, which is more than 1% of the world's power generation. There is no doubt that wind power generation plays an important role in electric power systems [1; 2]. However, wind power generation faces primary problems of intermittency and uncertainty. The randomness of wind is caused by a variety of factors, such as terrain, season, air pressure, temperature, and so on, which can cause many problems [3]. Fluctuations in wind speed and large-scale wind power grid-connected access threaten the safe and stable operation of the power system and have a bad effect on electricity dispatching, which increases the operating costs of the power system and means the economic benefits of wind power generation decrease. The experience of wind power operation in various countries shows that wind power forecasting (WPF) technology is one of the most effective ways to alleviate the pressure of power grids by adjusting peak-loads and reducing system reserve capacity. At present, wind power forecasting (WPF) systems can be divided into three categories, which include short-term forecasting, medium-term forecasting, and long-term forecasting [4]. Short-term forecasting predicts the wind power from several hours to one week, medium-term forecasting predicts the wind power from one week to one month, while long-term forecasting forecasts the wind power from several months to one year. WPF methods consist of physical approaches, statistic approaches, learning approaches, and combination forecasting approaches [5]. Physical approaches mainly consider physical quantities,such as numerical weather, wind turbine hub height, wind turbine thrust coefficient, and other technical parameters [6]. The aim is to find the optimal wind speed estimation at the hub height of the wind turbine and convert it into the output power of the wind farm according to the power curve of the wind turbine or wind farm. Statistical methods can be divided into two categories. One category establishes linear or nonlinear mapping of wind power system input and output by curve fitting and parameter estimation based on previous wind power, including the autoregressive moving average (ARMA) model [7], autoregressive integrated moving average (ARIMA) model [8], and generalized autoregressive conditional heterosked (GARCH) model [9]. The other category builds the regression model between meteorological observations and wind power. Examples include support vector regression (SVR) [10; 11; 12; 13], classification and regression tree (CART) [14], Gaussian process regression (GPR) [15], and ensemble learning [16]. The learning approaches are mainly based on the artificial neural network (ANN) [17], which uses meteorological observations provided by the wind farm to predict the wind power; examples include back propagation neural network (BPNN) [18; 19], radial basis function neural network (RBFNN) [20], deep belief network (DBN) [21], recurrent neural network (RNN) [22], long short-term memory (LSTM) [23], and other neural networks [24; 25; 26]. For ANN, the adjustment of parameters may have a great influence on the prediction results. Some optimization algorithms are also used in combination with neural networks to adjust the parameters of neural networks to achieve better prediction results [27]. Combination forecasting approaches are based on a variety of different prediction models to overcome the shortcomings of a single model and obtain higher accuracy by using the combination of different models' prediction results [28]. In this paper, we present a new WPF model based on XGBoost, weather similarity analysis, and feature engineering. We try to analyze the similarity of weather and assign meteorological observation data into several clusters.