IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i6p2079-2086.html
   My bibliography  Save this article

Using principal component and cluster analysis in the heating evaluation of the school building sector

Author

Listed:
  • Gaitani, N.
  • Lehmann, C.
  • Santamouris, M.
  • Mihalakakou, G.
  • Patargias, P.

Abstract

In the field of energy savings in buildings, the interest towards the school sector is deeply motivated: schools have standard energy demands and high levels of environmental comforts should be guaranteed. The University of Athens in collaboration with the School Authority of Greece undertook a complete program on energy classification and environmental quality of school buildings. Data on energy consumptions were gathered and analysed with the participation of 1100 schools from all the prefectures of Greece. The data have been provided by the school authority of the country (OSK), in collaboration with the management of each school building. With regards to the size of the building and the external climate variability (HDD-method) energy normalization techniques have been applied in order to homogenize the data set. An energy classification tool has been created through clustering techniques, using the collected data regarding the heating energy consumption and as a result five energy classes have been defined. To evaluate the potential energy conservation for each class, the typical characteristics of school buildings belonging to an energy class have to be identified. A new methodology based on the use of the principal components analysis (PCA) has been developed. The method allows to define in an accurate way the typical building of each energy class and thus to perform analysis on the potential energy savings for the specific group of school buildings. By reducing the dimensionality of the problem, a bi-dimensional graphic in the first two PCs coordinate system promotes the understanding of the correlation between the examined variables, as well as the determination of sub-groups of school buildings with similar characteristics. The typical school of seven variables sample is defined as the closest to the medians in the principal components' coordinate system.

Suggested Citation

  • Gaitani, N. & Lehmann, C. & Santamouris, M. & Mihalakakou, G. & Patargias, P., 2010. "Using principal component and cluster analysis in the heating evaluation of the school building sector," Applied Energy, Elsevier, vol. 87(6), pages 2079-2086, June.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:6:p:2079-2086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(09)00530-3
    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. Papakostas, K. & Kyriakis, N., 2005. "Heating and cooling degree-hours for Athens and Thessaloniki, Greece," Renewable Energy, Elsevier, vol. 30(12), pages 1873-1880.
    2. Perez, Yael Valerie & Capeluto, Isaac Guedi, 2009. "Climatic considerations in school building design in the hot-humid climate for reducing energy consumption," Applied Energy, Elsevier, vol. 86(3), pages 340-348, March.
    3. Santamouris, M. & Balaras, C.A. & Dascalaki, E. & Argiriou, A. & Gaglia, A., 1994. "Energy consumption and the potential for energy conservation in school buildings in Hellas," Energy, Elsevier, vol. 19(6), pages 653-660.
    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. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    2. Yunbo Yang & Rongling Li & Tao Huang, 2020. "Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting," Energies, MDPI, vol. 13(17), pages 1-20, August.
    3. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
    4. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    5. Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    6. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    7. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    8. Juan E. Pardo & Ana Mejías & Antonio Sartal, 2020. "Assessing the Importance of Biomass-Based Heating Systems for More Sustainable Buildings: A Case Study Based in Spain," Energies, MDPI, vol. 13(5), pages 1-19, February.
    9. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
    10. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
    11. William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
    12. Wadud, Zia & Royston, Sarah & Selby, Jan, 2019. "Modelling energy demand from higher education institutions: A case study of the UK," Applied Energy, Elsevier, vol. 233, pages 816-826.
    13. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    14. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    15. Llorenç Burgas & Joan Colomer & Joaquim Melendez & Francisco Ignacio Gamero & Sergio Herraiz, 2021. "Integrated Unfold-PCA Monitoring Application for Smart Buildings: An AHU Application Example," Energies, MDPI, vol. 14(1), pages 1-15, January.
    16. Di Leo, Senatro & Cosmi, Carmelina & Ragosta, Maria, 2015. "An application of multivariate statistical techniques to partial equilibrium models outputs: The analysis of the NEEDS-TIMES Pan European model results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 108-120.
    17. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    18. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    19. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.

    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. Jan Kočí & Jiří Maděra & Yulia Khmurovska & Petr Štemberk & Robert Černý, 2023. "A Fuzzy Logic Concept for Predicting the Seasonal Thermal Performance of Building Envelopes Based on Structural and Geographical Parameters," Energies, MDPI, vol. 16(23), pages 1-12, November.
    2. Capeluto, I. Guedi & Ochoa, Carlos E., 2014. "Simulation-based method to determine climatic energy strategies of an adaptable building retrofit façade system," Energy, Elsevier, vol. 76(C), pages 375-384.
    3. Yu, Jinghua & Yang, Changzhi & Tian, Liwei & Liao, Dan, 2009. "Evaluation on energy and thermal performance for residential envelopes in hot summer and cold winter zone of China," Applied Energy, Elsevier, vol. 86(10), pages 1970-1985, October.
    4. Svetlana Pushkar & Abraham Yezioro, 2022. "External Shading Devices: Should the Energy Standard Be Supplemented with a Production Stage?," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    5. Gelegenis, John J., 2009. "A simplified quadratic expression for the approximate estimation of heating degree-days to any base temperature," Applied Energy, Elsevier, vol. 86(10), pages 1986-1994, October.
    6. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    7. Anxiao Zhang & Regina Bokel & Andy Van den Dobbelsteen & Yanchen Sun & Qiong Huang & Qi Zhang, 2017. "The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China," Sustainability, MDPI, vol. 9(10), pages 1-19, September.
    8. Nematchoua, Modeste Kameni & Tchinda, René & Orosa, José A., 2014. "Thermal comfort and energy consumption in modern versus traditional buildings in Cameroon: A questionnaire-based statistical study," Applied Energy, Elsevier, vol. 114(C), pages 687-699.
    9. Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    10. Giuliano Dall'O' & Elisa Bruni & Angela Panza, 2013. "Improvement of the Sustainability of Existing School Buildings According to the Leadership in Energy and Environmental Design (LEED) ® Protocol: A Case Study in Italy," Energies, MDPI, vol. 6(12), pages 1-21, December.
    11. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.
    12. Heracleous, Chryso & Michael, Aimilios, 2018. "Assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climatic conditions," Energy, Elsevier, vol. 165(PB), pages 1228-1239.
    13. Nassipkul Dyussembekova & Nazym Temirgaliyeva & Dias Umyshev & Madina Shavdinova & Reiner Schuett & Damesh Bektalieva, 2022. "Assessment of Energy Efficiency Measures’ Impact on Energy Performance in the Educational Building of Kazakh-German University in Almaty," Sustainability, MDPI, vol. 14(16), pages 1-25, August.
    14. Wang, Yang & Zhao, Fu-Yun & Kuckelkorn, Jens & Liu, Di & Liu, Li-Qun & Pan, Xiao-Chuan, 2014. "Cooling energy efficiency and classroom air environment of a school building operated by the heat recovery air conditioning unit," Energy, Elsevier, vol. 64(C), pages 991-1001.
    15. Mihalakakou, G & Psiloglou, B & Santamouris, M & Nomidis, D, 2002. "Application of renewable energy sources in the Greek islands of the South Aegean Sea," Renewable Energy, Elsevier, vol. 26(1), pages 1-19.
    16. Georgopoulou, E. & Sarafidis, Y. & Mirasgedis, S. & Balaras, C.A. & Gaglia, A. & Lalas, D.P., 2006. "Evaluating the need for economic support policies in promoting greenhouse gas emission reduction measures in the building sector: The case of Greece," Energy Policy, Elsevier, vol. 34(15), pages 2012-2031, October.
    17. Ucar, Aynur & Balo, Figen, 2009. "Effect of fuel type on the optimum thickness of selected insulation materials for the four different climatic regions of Turkey," Applied Energy, Elsevier, vol. 86(5), pages 730-736, May.
    18. 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.
    19. Ahmed, T. & Muttaqi, K.M. & Agalgaonkar, A.P., 2012. "Climate change impacts on electricity demand in the State of New South Wales, Australia," Applied Energy, Elsevier, vol. 98(C), pages 376-383.
    20. Antoniadis, Christodoulos N. & Martinopoulos, Georgios, 2019. "Optimization of a building integrated solar thermal system with seasonal storage using TRNSYS," Renewable Energy, Elsevier, vol. 137(C), pages 56-66.

    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:6:p:2079-2086. 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.