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2023 Vol.17, Issue 6 Preview Page

Research Article

30 December 2023. pp. 411-421
Abstract
References
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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
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Cho, G., Yoo, J.H. (2021). Energy Performance Assessment for Remodeling Decision-Making based on Energy Usage Data of Existing Public Buildings in Seoul. Journal of the Korean solar energy society, 41(4), 63-72. 10.7836/kses.2021.41.4.063
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Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E. (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168-178. 10.1016/j.apenergy.2014.02.057
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Jung, H.J. (2021). A study on the Use of Outdoor Temperature Information for Evaluating Building Energy Savings. Journal of Climate Change Research, 12(1), 109-120. 10.15531/KSCCR.2021.12.1.109
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Kim, J., Kwak, Y., Mun, S.H., Huh, J.H. (2022). Electric energy consumption predictions for residential buildings: Impact of data-driven model and temporal resolution on prediction accuracy. Journal of Building Engineering, 62, 105361. 10.1016/j.jobe.2022.105361
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Kissock, J.K., Haberl, J.S., Claridge, D.E. (2003). Inverse modeling toolkit: Numerical algorithms. ASHRAE Transactions, 109, 425.
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Li, H., Szum, C., Lisauskas, S., Bekhit, A., Nesler, C., Snyder, S.C. (2019). Targeting Building Energy Efficiency Opportunities: An Opensource Analytical & Benchmarking Tool. ASHRAE Transactions, 125.
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Mathew, P.A., Dunn, L.N., Sohn, M.D., Mercado, A., Custudio, C., Walter, T. (2015). Big-data for building energy performance: Lessons from assembling a very large national database of building energy use. Applied Energy, 140, 85-93. 10.1016/j.apenergy.2014.11.042
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Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., Zhao, X. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027-1047. 10.1016/j.rser.2017.09.108
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Yoon, J.H. (2017). Classification of Energy Consumption Patterns in University Buildings Using Change Point Model and Analysis According to Energy Impact Factors. Journal of the architectural institute of Korea: Structure & Construction, 33(11), 71-78.
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Choi, K.W., Woo, H.J., Jeong, S.H., Lee, H.N., Leigh, S.B. (2015). Predicting annual energy consumption based on the Simple Linear Regression Analysis between building energy consumption and outdoor air temperature. Autumn annual conference of AIK, 13-14.
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Jeong, S.H., Son, E.J., Leigh, S.B. (2021). An Analysis of Energy Consumption Patterns in Building Using the Change Point Model. Autumn annual conference of AIK, 41(1), 297-300.
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 : 17
  • No :6
  • Pages :411-421
  • Received Date : 2023-11-30
  • Revised Date : 2023-12-09
  • Accepted Date : 2023-12-12
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