References
- D. B. Crawley, J. W. Hand, Contrasting the capabilities of building energy performance simulation programs, Building and Environment 2008, 43, 661-663. https://doi.org/10.1016/j.buildenv.2006.10.027
- W. Zeyu, R. S. Srinivasan, A review of artificial intelligence based building energy use prediction, Renewable and Sustainable Energy Reviews, 2016,75, 796-808.
- 백영렬, "건물의 열에너지 해석 프로그램" 기계저널, 42권 11호 //(Y. R. Beak, Thermal energy analysis program of building, Journal of Mechanical Science and Technology, 2002, 42, 20-21.)
- Y. Yuebin, W. Denchai, D. Yu, A Review of Fault Detection and Diagnosis Methodologies on Air-handling Units, Energy and Buildings, 2014, 82, 550-562. https://doi.org/10.1016/j.enbuild.2014.06.042
- 김현수, 최기원, 장지훈, 강경모, 이승복, "BEAT프로그램을 이용한 건물에너지 retrofit요소들 간의 에너지 절감 관계분석", 대한 건축학회, 구조계 33(2), 2017.2,97-105//(H.S. Kim, K.W. Choi, J.H. Jang, K.M. Kang, S.B. Leigh, Analyzing Energy Reduction Correlations among Factors of Building Energy Retrofit by using BEAT Program, Journal of the Architectural Institute of Korea Structure & Construction, 2017, 33, 97-105)
- D. Basak, S. Pal, D.C. Patranabis, Support vector regression. Neural Information Process, 2007,11, 23-24.
- K.J. Kim, S.B. Cho, Ensemble classifiers based on correlation analysis for DNA microarray classification. Neurocomputing, 2006, 70, 187-99. https://doi.org/10.1016/j.neucom.2006.03.002
- S. Kang, P. Kang, T. Ko, S. Cho, S.J. Rhee, K.S. Yu, An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. Expert Systems with Application, 2015,42(9), 4265-73. https://doi.org/10.1016/j.eswa.2015.01.042
- W.S. Parker, Predicting weather and climate: uncertainty, ensembles and probability. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 2010, 41(3), 263-72. https://doi.org/10.1016/j.shpsb.2010.07.006
- P.D. Wilde, The gap between predicted and measured energy performance of buildings, Automation in Construction, 2014, 41, 40-49. https://doi.org/10.1016/j.autcon.2014.02.009
- A.C. Menezes, A. Cripps, D. Bouchlaghem, Predicted vs. actual energy performance of non domestic buildings using post occupancy evaluation data to reduce the performance gap, Applied Energy, 2014, 97, 335-364.
- G.S. Olivia, T.A. Christopher, In-use monitoring of buildings: An overview and classification of evaluation methods. Energy and Building, 2015, 86, 179-189.
- J.H. Choi, V. Loftness, A. Aziz, Post-occupancy evaluation of 20office building as basis for future IEQ stanadards and guidelines. Energy and Buildings, 2012 46, 167-175. https://doi.org/10.1016/j.enbuild.2011.08.009
- Carbon trust, Closing the Gap: Lesson learned on realising the potential of low carbon building design, 2012
- NHBC Carbon Foundation, Compliance for tomorrow's new homes, A review of the modeling tool and assumption, Closing the gap between designed and built performance, 2010
- T. Catalina, J. Virgone, E. Blanco, Development and validation of regression models to predict monthly heating demand for residential buildings, Energy and Building, 2008, 40(10), 1825-1832. https://doi.org/10.1016/j.enbuild.2008.04.001
- 안기언, 김영진, 박철수, "설계단계에서 동적 건물에너지 성능 분석의 쟁점들", 대한건축학회 - 계획계 28(12), 2012.12, 361-369//(K.U. Ahn, Y.J, Kim, C.S. Park, Issues on Dynamic Building Energy Performance Assessment in Design Process, Journal of the Architectural Institute of Korea Planning & Design, 2012, 28(12), 361-369)
- A.E. Ben-Nakhi, M.A. Mahmoud, Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 2004, 45, 2127-41. https://doi.org/10.1016/j.enconman.2003.10.009
- X. Lv, T. Lu, C.J. Kibert, M. Viljanen, A novel dynamic modeling approach for predicting building, Applied Energy, 2014, 114, 91-103. https://doi.org/10.1016/j.apenergy.2013.08.093
- C. Fan, F. Xiao, S. Wang, Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques, Applied Energy, 2014, 127, 1-10. https://doi.org/10.1016/j.apenergy.2014.04.016
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