Abstract
Association rule mining algorithm based on support-confidence framework is widely applied to the optimization of main operating parameters value in thermal power plant. But some important potential knowledge is easy to be overlooked by the framework in the actual mining process. Moreover, the simulation experiments show that there is a great relationship between mining results and a given minimum support threshold. Thus a dynamic interestingness-support framework mining algorithm based on metarules guided is proposed by which parameters for multidimensional association rules can be determined. The new framework reduces the redundancy of results by metarule-guided mining. And it mainly screens association rules with the index of interestingness except support, so as to weaken the dependence between mining results and the minimum support threshold. What is more, a new similarity criterion is introduced in dividing production process condition, to avoid the single spherical cluster determined by the Euclidean distance. Therefrom overcome the shortness of traditional dividing. The simulation results show that the algorithm proposed in this paper can effectively tap out the rules. And the rules can correctly reflect the knowledge of the unit and improve the accuracy of main operation parameters value in thermal power plant.
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Huang, W., Jia, L., Peng, D. (2017). New Framework Mining Algorithm Based Main Operation Parameters Optimization in Power Plant. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_49
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