## 2 Data Analysis and Preprocessing  
### Analysis of Wind Power Characteristics  
#### 2.1.1 Comparison of Different Data Sources  
Wind power prediction is essentially a kind of time series prediction. The deep learning network for time series prediction can be applied as a model to wind power prediction [23; 24; 25]. However, the spatiotemporal correlation and characteristics of the data source itself are one of the important factors that affect the predictive performance of the model, and can also have a significant impact on the selection and design of the model. Therefore, it is necessary to study the characteristics of the data themselves. There are many types of time series data, involving multiple fields such as stocks, electricity, healthcare, and transportation. This paper selected the classic power-related datasets Electrical Transformer Temperature (ETT) and Electricity and Wind Power datasets for feature comparison.  
**ETT:** It aims to predict the future oil temperature of power transformers, which is a critical indicator in electric power long-term deployment. It includes two-year data from two separate counties in China. To explore the granularity problem, data collection was conducted at a sampling frequency of 15 min and 1 h, respectively, to form ETTh and ETTm datasets. The data for each time step are composed of target oil temperature and six power load characteristics.  
**Electricity:** The Electricity dataset is designed to predict future power loads and is an important reference factor for long-term planning of power systems. It includes the electricity load of 321 different types of customers for 2 years. The sampling period is in hours, and the load unit is kWh.  
**Wind Power:** The wind power dataset is the main dataset studied in this paper, which aims to predict the change in wind power in the future and help the power grid to complete power planning. It includes output data from a total of 20 wind farms within 2 years, as well as auxiliary information such as wind speed, temperature, pressure, humidity, etc. The sampling period is 15 min, and the power unit is MW.  
In order to visually observe the characteristics of the data themselves and facilitate comparison, all data sources were standardized by Z-Score method, setting the sampling period to 1 h and the sampling duration to 1 week. The comparison of data waveforms is in Figure 1a. It can be seen that the period of ETT data is long with slow change, and there is basically no violent fluctuation. The Electricity data exhibit significant periodicity and short-term repeatability. This is because there is a strong correlation between electricity load and electricity usage time. During the day, the electricity load is high, while, at night, the electricity load is low and cyclic. Unlike the two classic datasets mentioned above, wind power data have their own unique characteristics. The fluctuations are large and irregular. In the early stage, the power value is very small, but in the later stage, it rapidly increases, and, after maintaining it for a period of time, it quickly drops back. This phenomenon is also known as wind power climbing. This is because wind power is strongly correlated with wind resources, and wind itself has irregularity and volatility. Such data features may also result in better short-term predictions, but they can pose challenges to medium-long-term predictions.