Prediction Method of Distribution Network Area Based on Association Analysis and Machine Learning

Due to the large number of work orders, strong randomness and mutual correlation between work orders, the operation and maintenance of distribution network was difficult. To solve these problems, a method based on association analysis and machine learning was proposed to analyze the work order law of distribution network and predict the problem area. Firstly, the work orders that affect the operation status of the distribution network are found out, and then the association rules between the initial work orders were obtained by the proposed association analysis method, and then the most likely problem area is found by the confidence degree of the index. Finally, the effectiveness of the proposed method was verified by the actual data of the business system. The example results showed that the new method can describe the association rules between indicators more systematically and comprehensively, and the proposed method performs well in hit rate and accuracy.