All Issue

2022 Vol.16, Issue 4 Preview Page

Research Article

30 August 2022. pp. 273-284
Amasyali, K., El-Gohary, N.M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192-1205. 10.1016/j.rser.2017.04.095
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J.M., Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 1-22. 10.1186/s41044-016-0014-0
Humphrey, G.B., Gibbs, M.S., Dandy, G.C., Maier, H.R. (2016). A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623-640. 10.1016/j.jhydrol.2016.06.026
Joo, D.S., Choi, D.J., Park, H. (2000). The effects of data preprocessing in the determination of coagulant dosing rate. Water Research, 34(13), 3295-3302. 10.1016/S0043-1354(00)00067-1
Kim, J.H., Seong, N.C., Choi, W. (2019a). Modeling and optimizing a chiller system using a machine learning algorithm. Energies, 12(15), 2860. 10.3390/en12152860
Kim, J.H., Seong, N.C., Choi, W. (2019b). Cooling load forecasting via predictive optimization of a nonlinear autoregressive exogenous (NARX) neural network model. Sustainability, 11(23), 6535. 10.3390/su11236535
Kim, J.H., Seong, N.C., Choi, W. (2020). Forecasting the energy consumption of an actual air handling unit and absorption chiller using ANN models. Energies, 13(17), 4361. 10.3390/en13174361
Kim, J.H., Seong, N.C., Choi, W.C. (2021). Comparative Evaluation of Predicting Energy Consumption of Absorption Heat Pump with Multilayer Shallow Neural Network Training Algorithms. Buildings, 12(1), 13. 10.3390/buildings12010013
Lee, C.W., Seong, N.C., Choi, W.C. (2021). Performance Improvement and Comparative Evaluation of the Chiller Energy Consumption Forecasting Model Using Python. Journal of Korean Institute of Architectural Sustainable Environment and Building Systems, 15(3), 252-264.
Lee, Y.R., Yoon, Y.R., Moon, H.J. (2020). A model for classification of occupant behavior based on building environmental data by seasons. Journal of the Architectural Institute of Korea, 36(11), 239-245.
Oprea, S.V., Bâra, A. (2019). Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions. IEEE Access, 7, 177874-177889. 10.1109/ACCESS.2019.2958383
Seong, N.C., Choi, K.B., Choi, W.C. (2019). Development and Evaluation of Predictive Model for Fan Air Flow Rate According to Artificial Neural Network Input Variables. Journal of Korean Institute of Architectural Sustainable Environment and Building Systems, 13(3), 191-202.
Seong, N.C., Kim, J.H., Choi, W. (2020). Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm. Buildings, 10(11), 195. 10.3390/buildings10110195
Sung, J.H., Cho, Y.S. (2019). Machine learning approach for pattern analysis of energy consumption in factory. KIPS Transactions on Computer and Communication Systems, 8(4), 87-92.
Field, K., Deru, M., Studer, D. (2010). Using DOE commercial reference buildings for simulation studies. Proceedings of SimBuild, 4(1), 85-93.
Kim, D.E., Gofman, M. (2018). Comparison of shallow and deep neural networks for network intrusion detection. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 204-208. 10.1109/CCWC.2018.8301755
Seong, N.C., Hong, G. (2021). Development of web based platform for building energy saving and improvement performance using building data. Proceeding of AIK, 41(1), 677-677.
Tan, P.N., Steinbach, M., Kumar, V. (2016). Introduction to data mining. United Kingdom, London: Pearson Education India.
ASHRAE. (2014). ASHRAE Guideline 14: Measurement of Energy and Demand Savings, American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc., Atlanta, GA.
Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, B., Liu, B., Halverson, M., Winiarski, D., Rosenberg, M., Yazdanian, M., Huang, J., Crawley, D. (2011). U.S. Department of Energy Commercial Reference Building Models of the National Building Stock (NREL/TP-5500-46861). National Renewable Energy Laboratory Technical Report. 10.2172/1009264
Federal Energy Management Program (FEMP). (2008). M&V Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0. Technical report, U.S. Department of Energy Federal Energy Management Program, M&V Guideline.
IEA, U. (2019). Global status report for buildings and construction 2019. Paris, France: IEA, 2019.
International Performance Measurement and Verification Protocol (IPMVP). (2010). International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Vol.1, Technical Report, Efficiency Valuation Organization.
Ministry of Land, Infrastructure and Transport (MOLIT). (2017). Rules on the Building Equipment Standards, Ministry of Land, Infrastructure and Transport, Korea.
US Department of Energy (DOE). (2015). An assessment of energy technologies and research opportunities. Quadrennial Technology Review.
Building Energy Codes Program. (2022). Available at: prototype-building-models (Accessed: June 22, 2022).
MathWorks Korea. (2020). MATLAB & Simulink. Available at: (Accessed: June 21, 2022).
  • 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
Journal Informaiton Journal of Korean Institute of Architectural Sustainable Environment and Building Systems Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • NRF
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close