Power Load Detection And Analysis Based On Data Mining

With the continuous construction of the smart grid,the power management department can obtain more load data.How to dig out the power consumption behavior,status from these data,and predict the future power consumption trend is very important to ensure the safe and stable operation of the power grid.The rapid development of data mining technology provides theoretical support for realizing rapid and effective mining of power consumption information from power load data.Therefore,according to the characteristics of power load data and the user power consumption rules,an in-depth research on power abnormality detection and power load forecasting is conducted by combining the key technologies of data mining,and abnormal power consumption behavior detection method and ultra-short-term prediction method of power load are designed by using data mining technology respectively.The specific research contents are as follows.1.Analyze the status and major technologies of power load data mining.In order to improve the quality and efficiency of mining the complex power load data,a time series based method is designed to clean and repair the dirty data in the power load data based on the analysis of the existing data preprocessing technology.According to the characteristics of power load data,the overall scheme of power load detection and analysis is designed combining with data mining technology.2.According to the demand of power network anomaly detection,power anomalies are analyzed and classified into numerical abnormalities with fixed thresholds and behavioral anomalies with varying power status.For numerical anomalies,a threshold detection method is designed;for the detection of abnormal behavior,in view of the normal electricity situation,such as increase of electricity demand,which will affect the stationary detection method to the detection accuracy of abnormal behavior,a detection method based on density clustering for abnormal behavior of power usage is proposed according to the relationship between similar users.3.In order to carry out scientific power dispatching,a super short-term power load forecasting method based on time series density clustering is proposed based on the analysis of the basic ideas and methods of the existing power load forecasting.This method analyzes the user power consumption pattern and obtains the reference sequence of the load to be predicted.Then,the ultra-short-term load forecasting model is established based on the principle of near-small distance and regression,which realizes the ultra-short-term power load forecasting.The power load data is used to test and verify the load detection method.The test results show that this method can achieve abnormal power consumption detection and ultra-short-term power load forecasting,and obtain better detection and analysis results.The power load detection and analysis results can be checked and managed by the management software,which helps to improve the management level of the power grid and realizes the safe and stable operation of the power grid.