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2022 Vol.16, Issue 4 Preview Page

Research Article

An Analysis of the Effect of the Data Preprocess on the Performance of Building Load Prediction Model Using Multilayer Neural Networks

데이터 전처리 과정이 다층신경망을 이용한 건물 부하 예측 모델의 성능에 미치는 영향 분석

Nam-Chul Seong1
,
Goo-Pyo Hong2*
성 남철1
,
홍 구표2*
1Senior Researcher, Department of Architectural Engineering, Kangwon National University, Samcheok, Korea
2Assistant Professor, Department of Architectural Engineering, Kangwon National University, Samcheok, Korea
1강원대학교 건설융합학부 전임연구원
2강원대학교 건설융합학부 조교수
*Corresponding Author
30 August 2022. pp. 273-284
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Abstract

Data preprocess affects the performance improvement of predictive models using neural networks. However, previous studies have not analyzed the effects of data preproces. Therefore, in this study, the effect of data preprocess in a load prediction model using a multilayer neural network was analyzed. After constructing a multilayer neural network and generating learning data using a reference building, the performance of the predictive model was evaluated and compared through index of ASHRAE Guideline 14. As a result of the study, it was confirmed that the prediction results were improved through data preprocess. Compared to non-data preprocess models, the CvRMSE of models that performed data preprocess was improved from 21.47% to 10.74%, and MBE from 7.71% to 4.11%. It was found that the predictive performance improvement effect of data cleaning that removes missing values of data was greater than that of data transformation that converts the form of data.

Keywords
Multilayer neural network
Data Pre-process
Building Load
Prediction Model
키워드
다층신경망
데이터 전처리
건물 부하
예측 모델
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Information
  • Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
  • Publisher(Ko) :한국건축친환경설비학회
  • Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • Journal Title(Ko) :한국건축친환경설비학회논문집
  • Volume : 16
  • No :4
  • Pages :273-284
  • Received Date : 2022-07-12
  • Revised Date : 2022-07-21
  • Accepted Date : 2022-08-01
  • DOI :https://doi.org/10.22696/jkiaebs.20220024
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