점증적 입자 모델의 진화론적 설계에 근거한 에너지효율 예측

This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each conte...

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Bibliographic Details
Published in전기학회 논문지 P권, 67(1) Vol. 67P; no. 1; pp. 47 - 51
Main Authors 염찬욱(Chan-Uk Yeom), 곽근창(Keun-Chang Kwak)
Format Journal Article
LanguageKorean
Published 대한전기학회 2018
Subjects
전기공학
Incremental granular model
Genetic algorithm
Energy efficiency data
Linear regression
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ISSN1229-800X
2586-7792

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Summary:This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.
Bibliography:KISTI1.1003/JNL.JAKO201809951098934
ISSN:1229-800X
2586-7792