Abstract
Facing mountains of data in modern energy management system, Related operators need to use machine learning to derive corresponding knowledge to support its decision-making. In view of the above question, Based on Prism, FURIA and the J48 classifier, this paper used 10 fold cross validation on the energy management system for training a data table TP rate respectively were: 92%, 88% and 84%, Prism classifier produced 5 rules, FURIA classifier produced 4 rules, decision tree generated by J48 had 5 valid braches ∘ Rules generated by classifiers can provide decision-making guidance for energy management system, and accelerate decision-making response performance.
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Yuan, F., Li, X., Li-ming, W., Le-ping, P., Ying, S. (2011). Knowledge Discovery of Energy Management System Based on Prism, FURIA and J48. In: Ma, M. (eds) Communication Systems and Information Technology. Lecture Notes in Electrical Engineering, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21762-3_77
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DOI: https://doi.org/10.1007/978-3-642-21762-3_77
Publisher Name: Springer, Berlin, Heidelberg
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