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An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

Author

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  • Dongjun Suh

    (Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea)

  • Seongju Chang

    (KAIST Institute for Urban Space and Systems (KIUSS), KAIST, Daejeon 305-701, Korea)

Abstract

This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which affect the energy and water resource usage characteristics of Korean residential complexes. The proposed model precisely estimated the electricity, gas energy and water consumption for each examined residential complex. In terms of validation, the results showed the highest level of agreement with actually collected datasets. The model shows promising prospects in providing necessary estimations, not only for optimally scaling and sizing energy- and water-related infrastructures, but also to promote reliable energy and resource savings through greenhouse gas (GHG) reduction planning in multi-family housing complexes. The model could also be of use in framing guidelines for the better planning of national or regional energy and resource policies and for forming a foundation of decision-making with definite references regarding the facility management of each apartment complex to enhance the energy and resource use efficiency at these locations.

Suggested Citation

  • Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4497-4516:d:21411
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    1. Wenxiu Wang & Yaoqiu Kuang & Ningsheng Huang, 2011. "Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 4(12), pages 1-24, December.
    2. Wei Yu & Baizhan Li & Yarong Lei & Meng Liu, 2011. "Analysis of a Residential Building Energy Consumption Demand Model," Energies, MDPI, vol. 4(3), pages 1-13, March.
    3. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    4. Büyükalaca, Orhan & Bulut, Hüsamettin & YIlmaz, Tuncay, 2001. "Analysis of variable-base heating and cooling degree-days for Turkey," Applied Energy, Elsevier, vol. 69(4), pages 269-283, August.
    5. Lam, Joseph C & Li, Danny H.W, 1998. "Daylighting and energy analysis for air-conditioned office buildings," Energy, Elsevier, vol. 23(2), pages 79-89.
    6. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    7. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    8. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    9. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    10. Wan, K. S. Y. & Yik, F. H. W., 2004. "Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong," Applied Energy, Elsevier, vol. 77(1), pages 69-85, January.
    11. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    12. Haas, Reinhard & Schipper, Lee, 1998. "Residential energy demand in OECD-countries and the role of irreversible efficiency improvements," Energy Economics, Elsevier, vol. 20(4), pages 421-442, September.
    13. Nima Amjady & Farshid Keynia, 2011. "A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems," Energies, MDPI, vol. 4(3), pages 1-16, March.
    14. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    15. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
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    2. Seo, Dong-yeon & Koo, Choongwan & Hong, Taehoon, 2015. "A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design," Applied Energy, Elsevier, vol. 142(C), pages 66-79.
    3. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, vol. 10(7), pages 1-16, June.
    4. Oh, Jeongyoon & Koo, Choongwan & Hong, Taehoon & Cha, Seung Hyun, 2018. "An integrated model for estimating the techno-economic performance of the distributed solar generation system on building façades: Focused on energy demand and supply," Applied Energy, Elsevier, vol. 228(C), pages 1071-1090.
    5. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
    6. Koo, Choongwan & Park, Sungki & Hong, Taehoon & Park, Hyo Seon, 2014. "An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method," Applied Energy, Elsevier, vol. 115(C), pages 205-215.
    7. Dongjun Suh & Seongju Chang, 2014. "A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings," Energies, MDPI, vol. 7(11), pages 1-33, October.

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