IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i10p3073-3083.html
   My bibliography  Save this article

USE of the ANOVA approach for sensitive building energy design

Author

Listed:
  • Mechri, Houcem Eddine
  • Capozzoli, Alfonso
  • Corrado, Vincenzo

Abstract

The article presents a new approach in which the Analysis Of Variance (ANOVA) is used to identify the design variables that have the most impact on the variation of the building energy performance for a typical office building and to allocate the contribution of each variable to this variation. Moreover, the study addresses an important issue concerning the identification and the setting of a set of simple and concise variables that can be used during the conceptual design stage of office buildings. The analysis shows that the suggested approach could be useful for architects to evaluate the degree to which each design variable contributes to the variability of the building energy performance. Besides, the results may be helpful to support policymakers during the elaboration of energy codes by providing adequate information for the selection and handling of the parameters that control the variability of the energy needs.

Suggested Citation

  • Mechri, Houcem Eddine & Capozzoli, Alfonso & Corrado, Vincenzo, 2010. "USE of the ANOVA approach for sensitive building energy design," Applied Energy, Elsevier, vol. 87(10), pages 3073-3083, October.
  • Handle: RePEc:eee:appene:v:87:y:2010:i:10:p:3073-3083
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(10)00108-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kannan, R., 2009. "Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets," Applied Energy, Elsevier, vol. 86(10), pages 1873-1886, October.
    2. Hondo, Hiroki & Sakai, Shinsuke & Tanno, Shiro, 2002. "Sensitivity analysis of total CO2 emission intensities estimated using an input-output table," Applied Energy, Elsevier, vol. 72(3-4), pages 689-704, July.
    3. Tomlin, Alison. S., 2006. "The use of global uncertainty methods for the evaluation of combustion mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1219-1231.
    4. Kwon, Soon-Duck, 2010. "Uncertainty analysis of wind energy potential assessment," Applied Energy, Elsevier, vol. 87(3), pages 856-865, March.
    5. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    6. 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.
    7. Gustafsson, Stig-Inge, 1998. "Sensitivity analysis of building energy retrofits," Applied Energy, Elsevier, vol. 61(1), pages 13-23, September.
    8. Gorla, Rama S. R., 2004. "Probabilistic analysis of a solid-oxide fuel-cell based hybrid gas-turbine system," Applied Energy, Elsevier, vol. 78(1), pages 63-74, May.
    9. Yohanis, Y. G. & Norton, B., 1999. "Utilization factor for building solar-heat gain for use in a simplified energy model," Applied Energy, Elsevier, vol. 63(4), pages 227-239, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Ramkishore & Lazarus, I.J. & Kishore, V.V.N., 2016. "Uncertainty and sensitivity analyses of energy and visual performances of office building with external venetian blind shading in hot-dry climate," Applied Energy, Elsevier, vol. 184(C), pages 155-170.
    2. Yildiz, Yusuf & Korkmaz, Koray & Göksal Özbalta, Türkan & Durmus Arsan, Zeynep, 2012. "An approach for developing sensitive design parameter guidelines to reduce the energy requirements of low-rise apartment buildings," Applied Energy, Elsevier, vol. 93(C), pages 337-347.
    3. Ramkishore Singh & Dharam Buddhi & Samar Thapa & Chander Prakash & Rajesh Singh & Atul Sharma & Shane Sheoran & Kuldeep Kumar Saxena, 2022. "Sensitivity Analysis for Decisive Design Parameters for Energy and Indoor Visual Performances of a Glazed Façade Office Building," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
    4. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    5. Amirinia, Gholamreza & Mafi, Somayeh & Mazaheri, Said, 2017. "Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS," Renewable Energy, Elsevier, vol. 113(C), pages 915-929.
    6. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    7. Chaudry, Modassar & Abeysekera, Muditha & Hosseini, Seyed Hamid Reza & Jenkins, Nick & Wu, Jianzhong, 2015. "Uncertainties in decarbonising heat in the UK," Energy Policy, Elsevier, vol. 87(C), pages 623-640.
    8. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    9. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    11. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
    12. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    13. Baskut, Omer & Ozgener, Onder & Ozgener, Leyla, 2010. "Effects of meteorological variables on exergetic efficiency of wind turbine power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3237-3241, December.
    14. Krajacic, Goran & Duic, Neven & Carvalho, Maria da Graça, 2011. "How to achieve a 100% RES electricity supply for Portugal?," Applied Energy, Elsevier, vol. 88(2), pages 508-517, February.
    15. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    16. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    17. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    18. Cheng-Dar Yue & Yi-Shegn Chiu & Chien-Cheng Tu & Ta-Hui Lin, 2020. "Evaluation of an Offshore Wind Farm by Using Data from the Weather Station, Floating LiDAR, Mast, and MERRA," Energies, MDPI, vol. 13(1), pages 1-20, January.
    19. Avinash Vijay & Adam Hawkes, 2017. "The Techno-Economics of Small-Scale Residential Heating in Low Carbon Futures," Energies, MDPI, vol. 10(11), pages 1-23, November.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:87:y:2010:i:10:p:3073-3083. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.