IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i2p257-d90095.html
   My bibliography  Save this article

Energy Consumption Analysis for Concrete Residences—A Baseline Study in Taiwan

Author

Listed:
  • Kuo-Liang Lin

    (Department of Civil and Ecological Engineering, I-Shou University, Kaohsiung 84001, Taiwan)

  • Ming-Young Jan

    (Department of Civil and Ecological Engineering, I-Shou University, Kaohsiung 84001, Taiwan)

  • Chien-Sen Liao

    (Department of Civil and Ecological Engineering, I-Shou University, Kaohsiung 84001, Taiwan)

Abstract

Estimating building energy consumption is difficult because it deals with complex interactions among uncertain weather conditions, occupant behaviors, and building characteristics. To facilitate estimation, this study employs a benchmarking methodology to obtain energy baseline for sample buildings. Utilizing a scientific simulation tool, this study attempts to develop energy consumption baselines of two typical concrete residences in Taiwan, and subsequently allows a simplified energy consumption prediction process at an early design stage of building development. Using weather data of three metropolitan cities as testbeds, annual energy consumption of two types of modern residences are determined through a series of simulation sessions with different building settings. The impacts of key building characteristics, including building insulation, air tightness, orientation, location, and residence type, are carefully investigated. Sample utility bills are then collected to validate the simulated results, resulting in three adjustment parameters for normalization, including ‘number of residents’, ‘total floor area’, and ‘air conditioning comfort level’, for justification of occupant behaviors in different living conditions. Study results not only provide valuable benchmarking data serving as references for performance evaluation of different energy-saving strategies, but also show how effective extended building insulation, enhanced air tightness, and prudent selection of residence location and orientation can be for successful implementation of building sustainability in tropical and subtropical regions.

Suggested Citation

  • Kuo-Liang Lin & Ming-Young Jan & Chien-Sen Liao, 2017. "Energy Consumption Analysis for Concrete Residences—A Baseline Study in Taiwan," Sustainability, MDPI, vol. 9(2), pages 1-13, February.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:2:p:257-:d:90095
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/2/257/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/2/257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ki Uhn Ahn & Deuk Woo Kim & Young Jin Kim & Seong Hwan Yoon & Cheol Soo Park, 2016. "Issues to Be Solved for Energy Simulation of An Existing Office Building," Sustainability, MDPI, vol. 8(4), pages 1-12, April.
    2. Canyurt, Olcay Ersel & Ozturk, Harun Kemal & Hepbasli, Arif & Utlu, Zafer, 2005. "Estimating the Turkish residential-commercial energy output based on genetic algorithm (GA) approaches," Energy Policy, Elsevier, vol. 33(8), pages 1011-1019, May.
    3. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    4. Henry Hsieh, 2005. "The 1990s Taiwan residential construction boom: a supply side interpretation," Construction Management and Economics, Taylor & Francis Journals, vol. 23(3), pages 265-284.
    5. Jim, C.Y., 2014. "Air-conditioning energy consumption due to green roofs with different building thermal insulation," Applied Energy, Elsevier, vol. 128(C), pages 49-59.
    6. 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.
    7. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Perry C. Y. Liu & Huai-Wei Lo & James J. H. Liou, 2020. "A Combination of DEMATEL and BWM-Based ANP Methods for Exploring the Green Building Rating System in Taiwan," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    2. Dany Perwita Sari & Yun-Shang Chiou, 2019. "Do Energy Conservation Strategies Limit the Freedom of Architecture Design? A Case Study of Minsheng Community, Taipei, Taiwan," Sustainability, MDPI, vol. 11(7), pages 1-23, April.

    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. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Zu, Kan & Qin, Menghao, 2021. "Experimental and modeling investigation of water adsorption of hydrophilic carboxylate-based MOF for indoor moisture control," Energy, Elsevier, vol. 228(C).
    3. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    4. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    5. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    6. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
    7. Longo, Stefano & d’Antoni, Benedetto Mirko & Bongards, Michael & Chaparro, Antonio & Cronrath, Andreas & Fatone, Francesco & Lema, Juan M. & Mauricio-Iglesias, Miguel & Soares, Ana & Hospido, Almudena, 2016. "Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement," Applied Energy, Elsevier, vol. 179(C), pages 1251-1268.
    8. Mauricio Nath Lopes & Roberto Lamberts, 2018. "Development of a Metamodel to Predict Cooling Energy Consumption of HVAC Systems in Office Buildings in Different Climates," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
    9. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    10. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    11. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    12. Ferdinando Salata & Anna Tarsitano & Iacopo Golasi & Emanuele De Lieto Vollaro & Massimo Coppi & Andrea De Lieto Vollaro, 2016. "Application of Absorption Systems Powered by Solar Ponds in Warm Climates for the Air Conditioning in Residential Buildings," Energies, MDPI, vol. 9(10), pages 1-18, October.
    13. Ríos-Ocampo, J.P. & Olaya, Y. & Osorio, A. & Henao, D. & Smith, R. & Arango-Aramburo, S., 2022. "Thermal districts in Colombia: Developing a methodology to estimate the cooling potential demand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    14. Yu, Xinqiao & Yan, Da & Sun, Kaiyu & Hong, Tianzhen & Zhu, Dandan, 2016. "Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings," Applied Energy, Elsevier, vol. 183(C), pages 725-736.
    15. Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    16. Chong, Daokun & Zhu, Neng & Luo, Wei & Zhang, Zhiyu, 2019. "Broadening human thermal comfort range based on short-term heat acclimation," Energy, Elsevier, vol. 176(C), pages 418-428.
    17. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    18. 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.
    19. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    20. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.

    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:gam:jsusta:v:9:y:2017:i:2:p:257-:d:90095. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.