1 |
D. B. Crawley, J. W. Hand, Contrasting the capabilities of building energy performance simulation programs, Building and Environment 2008, 43, 661-663.
DOI
|
2 |
W. Zeyu, R. S. Srinivasan, A review of artificial intelligence based building energy use prediction, Renewable and Sustainable Energy Reviews, 2016,75, 796-808.
|
3 |
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.)
|
4 |
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.
DOI
|
5 |
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)
|
6 |
D. Basak, S. Pal, D.C. Patranabis, Support vector regression. Neural Information Process, 2007,11, 23-24.
|
7 |
K.J. Kim, S.B. Cho, Ensemble classifiers based on correlation analysis for DNA microarray classification. Neurocomputing, 2006, 70, 187-99.
DOI
|
8 |
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.
DOI
|
9 |
X. Lv, T. Lu, C.J. Kibert, M. Viljanen, A novel dynamic modeling approach for predicting building, Applied Energy, 2014, 114, 91-103.
DOI
|
10 |
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.
DOI
|
11 |
P.D. Wilde, The gap between predicted and measured energy performance of buildings, Automation in Construction, 2014, 41, 40-49.
DOI
|
12 |
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.
|
13 |
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.
|
14 |
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.
DOI
|
15 |
Carbon trust, Closing the Gap: Lesson learned on realising the potential of low carbon building design, 2012
|
16 |
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
|
17 |
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.
DOI
|
18 |
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)
|
19 |
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.
DOI
|
20 |
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.
DOI
|