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Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods

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  • Wang, Lan
  • Lee, Eric W.M.
  • Hussian, Syed Asad
  • Yuen, Anthony Chun Yin
  • Feng, Wei

Abstract

Residential buildings consume a large amount of energy in operation, which can be reduced by planning energy-efficient design and operation strategies. Understanding and quantifying the impact of key driving factors to the residential building energy end-use is essential for promoting more energy-efficient building design and operation schemes. Although the climate, building, and occupant-related features have been proven as the key driving factors to residential building energy end-use, their impacts are rarely compared and quantified simultaneously. This study conducts the first attempt in combining the machine learning method with Monte Carlo method to quantify the impacts of these key driving factors simultaneously. Data collected from the Residential Energy Consumption Survey 2015 of the U.S. was investigated in this study. The results indicate that the total energy end-use has positive correlations with the total building area, rooms’ numbers, windows’ numbers, indoor heating temperature setpoint and the occupants’ age; and has a negative correlation with the cooling temperature setpoint; the impacts of heating degree days and cooling degree days are nonlinear and complicated; the impact of the level of insulation is nuanced and offset by the harsh climate. The results of this study contribute good references to policymakers and architects for the synthesis of energy-efficient residential building design and operation; guidelines can be developed for the future survey on residential building energy for relevant and precise data collection to improve the building energy modelling.

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  • Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007157
    DOI: 10.1016/j.apenergy.2021.117303
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    as
    1. Hamburg, Anti & Kuusk, Kalle & Mikola, Alo & Kalamees, Targo, 2020. "Realisation of energy performance targets of an old apartment building renovated to nZEB," Energy, Elsevier, vol. 194(C).
    2. Chang, Yuan & Ries, Robert J. & Wang, Yaowu, 2013. "Life-cycle energy of residential buildings in China," Energy Policy, Elsevier, vol. 62(C), pages 656-664.
    3. Nan Zhou & Nina Khanna & Wei Feng & Jing Ke & Mark Levine, 2018. "Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050," Nature Energy, Nature, vol. 3(11), pages 978-984, November.
    4. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    5. Zhang, Junyi & Teng, Fei & Zhou, Shaojie, 2020. "The structural changes and determinants of household energy choices and energy consumption in urban China: Addressing the role of building type," Energy Policy, Elsevier, vol. 139(C).
    6. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    7. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    8. Numan, M. Y. & Almaziad, F. A. & Al-Khaja, W. A., 1999. "Architectural and urban design potentials for residential building energy saving in the Gulf region," Applied Energy, Elsevier, vol. 64(1-4), pages 401-410, September.
    9. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    10. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    11. van Hooff, T. & Blocken, B. & Timmermans, H.J.P. & Hensen, J.L.M., 2016. "Analysis of the predicted effect of passive climate adaptation measures on energy demand for cooling and heating in a residential building," Energy, Elsevier, vol. 94(C), pages 811-820.
    12. Pacheco, R. & Ordóñez, J. & Martínez, G., 2012. "Energy efficient design of building: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3559-3573.
    13. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    14. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    15. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    16. Soltani, Mohammad & Rahmani, Omeid & Ghasimi, Dara S.M. & Ghaderpour, Yousef & Pour, Amin Beiranvand & Misnan, Siti Hajar & Ngah, Ibrahim, 2020. "Impact of household demographic characteristics on energy conservation and carbon dioxide emission: Case from Mahabad city, Iran," Energy, Elsevier, vol. 194(C).
    17. 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.
    18. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    19. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    20. Gabriela Reus-Netto & Pilar Mercader-Moyano & Jorge D. Czajkowski, 2019. "Methodological Approach for the Development of a Simplified Residential Building Energy Estimation in Temperate Climate," Sustainability, MDPI, vol. 11(15), pages 1-27, July.
    21. Franz Fuerst & Dimitra Kavarnou & Ramandeep Singh & Hassan Adan, 2020. "Determinants of energy consumption and exposure to energy price risk: a UK study [Determinanten des Energieverbrauchs und Energiepreisrisiko: Eine Studie aus Großbritannien]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 6(1), pages 65-80, April.
    22. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    23. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    24. Stephan, André & Crawford, Robert H., 2016. "The relationship between house size and life cycle energy demand: Implications for energy efficiency regulations for buildings," Energy, Elsevier, vol. 116(P1), pages 1158-1171.
    25. Saidur, R. & Masjuki, H.H. & Jamaluddin, M.Y., 2007. "An application of energy and exergy analysis in residential sector of Malaysia," Energy Policy, Elsevier, vol. 35(2), pages 1050-1063, February.
    26. Zou, Baoling & Luo, Biliang, 2019. "Rural household energy consumption characteristics and determinants in China," Energy, Elsevier, vol. 182(C), pages 814-823.
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    3. Rafael Campamà Pizarro & Ricardo Bernardo & Maria Wall, 2023. "Streamlining Building Energy Modelling Using Open Access Databases—A Methodology towards Decarbonisation of Residential Buildings in Sweden," Sustainability, MDPI, vol. 15(5), pages 1-17, February.

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