An approach based on decision tree for analysis of behavior with combined cycle power plant

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI:10.1109/PIC.2017.8359583

Abshukirov Zhandos, Jian Guo

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引用次数: 5

Abstract

This paper presents about Combined Cycle Power Plant (CCPP) and decision tree. CCPP considered as the best effective power suppliers to the large temperature incline between its gas turbine passage and the environment or the cooling process, and to help of their engineers, who are able to optimally venture the present temperature level. Moreover, in this paper we did comparison of four types of decision tree algorithms like Decision Stump, Hoeffding Tree, logistic model trees (LMT) and J48. Based on these algorithms we analyzed the behavior of Combined Cycle Power Plant (CCPP), particularly its temperature. The temperature is target variable among other variables. This is why temperature was divided three classes. Theoretical analysis and experimental results have shown that the J48 algorithm is the best algorithm which predict attributes of given instances precisely among other three algorithms. Based on our findings, J48 algorithm can predict precisely the temperature of Combined Cycle Power Plant and predict 97.1676% cases correctly.

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基于决策树的联合循环电厂行为分析方法

本文介绍了联合循环电厂(CCPP)及其决策树。CCPP被认为是其燃气轮机通道与环境或冷却过程之间的大温度倾斜的最佳有效电源供应商，并帮助他们的工程师，他们能够最佳地冒险当前的温度水平。此外，本文还对decision Stump、Hoeffding tree、logistic model trees (LMT)和J48四种决策树算法进行了比较。在此基础上，对联合循环电厂的运行行为进行了分析，特别是对其温度进行了分析。温度是众多变量中的目标变量。这就是温度被分为三类的原因。理论分析和实验结果表明，在三种算法中，J48算法能准确地预测给定实例的属性，是最好的算法。研究结果表明，J48算法能准确预测联合循环电厂的温度，预测正确率为97.1676%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 International Conference on Progress in Informatics and Computing (PIC)

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