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      지역난방 동절기 공동주택 온수급탕부하의 LS-SVM 기반 모델링

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      https://www.riss.kr/link?id=A102064511

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      다국어 초록 (Multilingual Abstract)

      Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be...

      Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system’s hot water supply load, using the unit heating area’s hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10~20% in MAPE.

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      목차 (Table of Contents)

      • Abstract
      • 1. 서론
      • 2. LS-SVM
      • 3. 동절기 온수급탕부하의 모델링
      • 4. 결론
      • Abstract
      • 1. 서론
      • 2. LS-SVM
      • 3. 동절기 온수급탕부하의 모델링
      • 4. 결론
      • References
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      참고문헌 (Reference)

      1 Suykens, "Weighted least squares support vector machines : robustness and space approximation" 48 (48): 85-105, 2002

      2 Koive, T. A, "Trends in domestic hot water consumption in Estonian apartment building" 12 (12): 72-80, 2006

      3 Werner, S. E., "The heat load in district heating systems" Chalmers University of Technology 1984

      4 Vapnik, V, "The Nature of Statistical Learning Theory" Springer 1995

      5 박영칠, "ReducedLS-SVM을 이용한 지역난방 동절기 공동주택 난방부하의 모델링" 대한설비공학회 27 (27): 283-292, 2015

      6 Suykens, J. A. K, "Least square support vector machine" World Science Pub 2002

      7 Gavin et al., "Improved sparse least squares support vector machines" 48 (48): 1025-1031, 2002

      8 Heller, A. J., "Heat load modeling for large systems" 72 (72): 371-387, 2002

      9 Baudat, G., "Feature vector selection and projection using kernels" 55 (55): 21-38, 2003

      10 An, S., "Fast cross validation algorithms for least squares support vector machines and kernel ridge regression" 40 (40): 2154-2162, 2007

      1 Suykens, "Weighted least squares support vector machines : robustness and space approximation" 48 (48): 85-105, 2002

      2 Koive, T. A, "Trends in domestic hot water consumption in Estonian apartment building" 12 (12): 72-80, 2006

      3 Werner, S. E., "The heat load in district heating systems" Chalmers University of Technology 1984

      4 Vapnik, V, "The Nature of Statistical Learning Theory" Springer 1995

      5 박영칠, "ReducedLS-SVM을 이용한 지역난방 동절기 공동주택 난방부하의 모델링" 대한설비공학회 27 (27): 283-292, 2015

      6 Suykens, J. A. K, "Least square support vector machine" World Science Pub 2002

      7 Gavin et al., "Improved sparse least squares support vector machines" 48 (48): 1025-1031, 2002

      8 Heller, A. J., "Heat load modeling for large systems" 72 (72): 371-387, 2002

      9 Baudat, G., "Feature vector selection and projection using kernels" 55 (55): 21-38, 2003

      10 An, S., "Fast cross validation algorithms for least squares support vector machines and kernel ridge regression" 40 (40): 2154-2162, 2007

      11 Evarts, J. C, "Domestic hot water consumption estimates for solar heating thermal sizing" 58 : 58-65, 2013

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2022 평가예정 계속평가 신청대상 (등재유지)
      2017-01-01 평가 우수등재학술지 선정 (계속평가)
      2013-01-01 평가 등재 1차 FAIL (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2001-07-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.8 0.8 0.62
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.51 0.44 0.622 0.03
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