Research On Data Mining Technologies Applied In Energy Efficiency Management Of Industrial Park

An industrial park is built by the government department on the need of economic development, as the developing of a new focus of the regional economic, industrial parks have grown up rapidly. Industrial parks which run in the tradition modes have the features of overloading in electricity, if the usage in electricity can be controlled properly, it will act as a positive role in orderly operation of power grids. As a consequence, the paper utilizes management of energy efficiency system in the power load situation of the enterprises in the industrial park. However, our country’s current energy efficiency management policies give priority to orderly power utility with administrative measures, thus creating the ignorance of production actuality and the electricity characteristics of users. Aiming at this situation, the paper represents fine clustering algorithm to master users’electricity usage, hoping to achieve the goal of controlling power load in a premise of reducing the impact on production.The main work of the paper is as follows:(1) Propose the model framework of energy efficiency management system oriented to an industrial park. The proposed framework adds user feedback links, and the interaction with users will be more benign and humanized.(2) Propose the fine clustering algorithm suited to the industrial park. The sample used in study has a large amount of data, and its vector dimension is much. In the study, the system clustering method and the min-max k-means clustering algorithm are combined, generating a fine clustering algorithm. The fine clustering algorithm can get stable and accurate experimental results.(3) Propose the pre-processing method aiming at particular datasets. According to the characteristics of sample data, preprocessing process consists of three aspects: transverse recognition, length ways-comparison scheme and temperature recognition. According to the importance of the load in different periods, different weights are set separately.The experimental analysis shows that the same field of enterprise on the electrical behavior is not necessarily the same, but different fields of the enterprises exist certain similarity in using electricity, the results provide proper basis for energy efficiency management.