# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 1 Introduction  
With the rapid development and large-scale integration of wind power, accurate and reliable wind power forecasting (WPF) plays a key role in helping power system operators and market operators to schedule and trade wind generation at various spatial and temporal scales. Highly penetrated and correlated wind power integrated into the power system implies great uncertainties and technical challenges for system operators. Therefore, short-term WPF is one of the most important solutions to promote the development of wind energy, with the advantages of helping electric dispatching and restraining the impact of fluctuations of wind power effectively. Most of the existing WPF methods have been optimized by hybrid different algorithms in recent years. Reference [1] evaluated the effectiveness of autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) approaches for modeling the mean and volatility of wind speed. Reference [2] analyzed the Kalman filter to the configuration for wind speed and wind power forecast, while reference [3] proposed a real-time forecasting method of wind speed based on spatio-temporal correlation and the BP neural network, which improved the prediction accuracy. Besides, in the literature [4; 5; 6; 7], the optimization and combination of the neural network algorithm, empirical mode decomposition and regression vector machine were used to predict windpower. Even though the combination forecasting model improved forecasting accuracy to some extent, the results were still unsatisfactory. After in-depth research, we find scholars have been paying attention to researching WPF while screening the similar set as prediction samples referring to the data mining and fine-analysis of samples. According to the analysis principles, the existing analysis methods based on data mining and identifying similar samples are divided into three:  
* Predicting periodicity based on monthly or quarterly historical meteorological conditions [8];
* Classification of meteorological data by clustering algorithms according to similar wind conditions, weather patterns and output patterns [9]; and
* Application of distance function and entropy correlation based on the characteristics of wind speed to obtain similar samples [10]. While most works have looked at how to predict wind power generation at a given site or for a given portfolio, only few have considered the spatio-temporal correlation in their power generation. Research on the methods above indicate that the training samples are mostly selected from historical data, with a definite aim and limited number to study a similar set. However, there are lots of redundant data in samples, on account of ignoring the diversity of meteorological data changes and the continuity of wind speed in a short time. The problem we have outlined deals largely with the study of data pre-processing. It is necessary to combine the variability of actual weather conditions and the continuity of short times to obtain the accurate sample data, so as to improve the accuracy of forecasting through the intentionally screened and fine-analysis of historical data. In this paper, we focus on original data preprocessing, which includes removing redundant data and supplementing missing data. This is not because redundant data will lead to much more calculational burden and slow down the calculation speed, but because missing data may cause the loss of effective information and reduce the prediction accuracy. To improve the accuracy of WPF, it is important to consider the spatio-temporal correlation between the meteorological data in the same time series when supplementing the missing data. To solve these problems, we present a short-term WPF approach based on the data mining of historical wind sources. The paper is organized as follows. Section 2 describes the structure of the Lomaofski optimization model, which is used to pre-process the data. The continuous time similarity clustering method (CTSC) is introduced in Section 3.