Journal of System Simulation
Abstract
Abstract: The data mining technology for historical data of power plant has the problem of low efficiency as the data dimension is high and data size is large. Some parameters are set without theoretical guidance and the objective parameter value is not reasonably determined in the corresponding algorithm of data mining. A mining algorithm with improved quantitative association rule based on Apriori is proposed. Aiming at the economical operation of power plant, target guidance is used to constrain the dimension and compress the quantity in sample space, which improves the mining efficiency and determines the parameter’s target value reasonably. The operation data of a 300MW unit is analyzed and its results show that the improved quantitative association rule algorithm can improve the efficiency of data mining and determine the parameter value more accurately.
Recommended Citation
Huang, Wencheng; Li, Jia; Peng, Daogang; and Wang, Li
(2019)
"Apriori-Based Association Rule Algorithm and Its Application in Power Plant,"
Journal of System Simulation: Vol. 30:
Iss.
1, Article 34.
DOI: 10.16182/j.issn1004731x.joss.201801034
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/34
First Page
266
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201801034
Last Page
271
CLC
TP274
Recommended Citation
Huang Wencheng, Jia Li, Peng Daogang, Li Wang. Apriori-Based Association Rule Algorithm and Its Application in Power Plant[J]. Journal of System Simulation, 2018, 30(1): 266-271.
DOI
10.16182/j.issn1004731x.joss.201801034
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