IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i2p551-557.html
   My bibliography  Save this article

Artificial neural networks for energy analysis of office buildings with daylighting

Author

Listed:
  • Wong, S.L.
  • Wan, Kevin K.W.
  • Lam, Tony N.T.

Abstract

An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters - four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash-Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:2:p:551-557
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(09)00266-9
    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. 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.
    2. Li, D.H.W & Lam, J.C & Wong, S.L, 2002. "Daylighting and its implications to overall thermal transfer value (OTTV) determinations," Energy, Elsevier, vol. 27(11), pages 991-1008.
    3. Loutzenhiser, Peter G. & Maxwell, Gregory M. & Manz, Heinrich, 2007. "An empirical validation of the daylighting algorithms and associated interactions in building energy simulation programs using various shading devices and windows," Energy, Elsevier, vol. 32(10), pages 1855-1870.
    4. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    5. Li, Danny H.W. & Lam, Tony N.T. & Wong, S.L. & Tsang, Ernest K.W., 2008. "Lighting and cooling energy consumption in an open-plan office using solar film coating," Energy, Elsevier, vol. 33(8), pages 1288-1297.
    6. Yu, Philip C.H. & Chow, W.K., 2001. "Energy use in commercial buildings in Hong Kong," Applied Energy, Elsevier, vol. 69(4), pages 243-255, August.
    7. 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.
    8. 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.
    9. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
    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. 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.
    2. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    3. Stevanović, Sanja, 2016. "Parametric study of a cost-optimal, energy efficient office building in Serbia," Energy, Elsevier, vol. 117(P2), pages 492-505.
    4. Li, Danny H.W. & Chau, Natalie T.C. & Wan, Kevin K.W., 2013. "Predicting daylight illuminance and solar irradiance on vertical surfaces based on classified standard skies," Energy, Elsevier, vol. 53(C), pages 252-258.
    5. 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.
    6. Antanasijević, Davor & Pocajt, Viktor & Ristić, Mirjana & Perić-Grujić, Aleksandra, 2015. "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks," Energy, Elsevier, vol. 84(C), pages 816-824.
    7. Li, Danny H.W., 2010. "A review of daylight illuminance determinations and energy implications," Applied Energy, Elsevier, vol. 87(7), pages 2109-2118, July.
    8. Probst, Oliver, 2004. "Cooling load of buildings and code compliance," Applied Energy, Elsevier, vol. 77(2), pages 171-186, February.
    9. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    10. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
    11. Yang, Liu & Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "A new method to develop typical weather years in different climates for building energy use studies," Energy, Elsevier, vol. 36(10), pages 6121-6129.
    12. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    13. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
    14. Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "Assessment of climate change impact on building energy use and mitigation measures in subtropical climates," Energy, Elsevier, vol. 36(3), pages 1404-1414.
    15. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
    16. Li, Danny H.W. & Lam, Tony N.T. & Wong, S.L. & Tsang, Ernest K.W., 2008. "Lighting and cooling energy consumption in an open-plan office using solar film coating," Energy, Elsevier, vol. 33(8), pages 1288-1297.
    17. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    18. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    19. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
    20. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.

    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:2:p:551-557. 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.