| Title |
Energy Efficiency Prediction Based on an Evolutionary Design of Incremental Granular Model |
| Authors |
염찬욱(Chan-Uk Yeom) ; 곽근창(Keun-Chang Kwak) |
| DOI |
http://doi.org/10.5370/KIEEP.2018.67.1.047 |
| Keywords |
Incremental granular model ; Genetic algorithm ; Energy efficiency data ; Linear regression |
| Abstract |
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. |