IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v224y2018icp538-549.html
   My bibliography  Save this article

A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada

Author

Listed:
  • Díaz, Julián Arco
  • Ramos, José Sánchez
  • Delgado, M. Carmen Guerrero
  • García, David Hidalgo
  • Montoya, Francisco Gil
  • Domínguez, Servando Álvarez

Abstract

Energy consumption in the building sector presents a high potential for reducing it by means of interventions to improve the energy efficiency of the building and/or its installations. After these interventions, it is necessary to ensure the energy impact expected by measurement and verification of savings protocols.

Suggested Citation

  • Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    • Handle: RePEc:eee:appene:v:224:y:2018:i:c:p:538-549
    DOI: 10.1016/j.apenergy.2018.04.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918306184
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.04.060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Mikulić, Davor & Bakarić, Ivana Rašić & Slijepčević, Sunčana, 2016. "The economic impact of energy saving retrofits of residential and public buildings in Croatia," Energy Policy, Elsevier, vol. 96(C), pages 630-644.
    2. Trotta, Gianluca, 2018. "Factors affecting energy-saving behaviours and energy efficiency investments in British households," Energy Policy, Elsevier, vol. 114(C), pages 529-539.
    3. Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
    4. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    5. Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
    6. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    7. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Development of a prediction model for the cost saving potentials in implementing the building energy efficiency rating certification," Applied Energy, Elsevier, vol. 189(C), pages 257-270.
    8. Chen, G.Q. & Wu, X.F., 2017. "Energy overview for globalized world economy: Source, supply chain and sink," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 735-749.
    9. Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
    10. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    11. De Rosa, Mattia & Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach," Applied Energy, Elsevier, vol. 128(C), pages 217-229.
    12. Juaidi, Adel & AlFaris, Fadi & Montoya, Francisco G. & Manzano-Agugliaro, Francisco, 2016. "Energy benchmarking for shopping centers in Gulf Coast region," Energy Policy, Elsevier, vol. 91(C), pages 247-255.
    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. Severinsen, A. & Myrland, Ø., 2022. "Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. Echarri-Iribarren, Victor & Echarri-Iribarren, Fernando & Rizo-Maestre, Carlos, 2019. "Ceramic panels versus aluminium in buildings: Energy consumption and environmental impact assessment with a new methodology," Applied Energy, Elsevier, vol. 233, pages 959-974.

    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. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    2. Liu, Jiangyan & Chen, Huanxin & Liu, Jiahui & Li, Zhengfei & Huang, Ronggeng & Xing, Lu & Wang, Jiangyu & Li, Guannan, 2017. "An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information," Applied Energy, Elsevier, vol. 206(C), pages 193-205.
    3. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
    4. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    5. Li, Yilin & Chen, Bin & Li, Chaohui & Li, Zhi & Chen, Guoqian, 2020. "Energy perspective of Sino-US trade imbalance in global supply chains," Energy Economics, Elsevier, vol. 92(C).
    6. Chun-Hsi Vivian Chen & Yu-Cheng Chen, 2021. "Assessment of Enhancing Employee Engagement in Energy-Saving Behavior at Workplace: An Empirical Study," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    7. Le Thi Dieu Hien & Khuu Ngoc Huyen & Thi Hong Loc Hoang, 2023. "Factors Affecting Energy-Saving Intentions among Youth in Vietnam," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 603-609, November.
    8. Faure, Corinne & Schleich, Joachim, 2020. "Poor energy ratings when appliances convey?," Energy Policy, Elsevier, vol. 139(C).
    9. Agnieszka Malec & Tomasz Cholewa & Alicja Siuta-Olcha, 2021. "Influence of Cold Water Inlets and Obstacles on the Energy Efficiency of the Hot Water Production Process in a Hot Water Storage Tank," Energies, MDPI, vol. 14(20), pages 1-26, October.
    10. Omar, M.N. & Samak, A.A. & Keshek, M.H. & Elsisi, S.F., 2020. "Simulation and validation model for using the energy produced from broiler litter waste in their house and its requirement of energy," Renewable Energy, Elsevier, vol. 159(C), pages 920-928.
    11. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    12. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    13. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    14. Wu, X.D. & Guo, J.L. & Chen, G.Q., 2018. "The striking amount of carbon emissions by the construction stage of coal-fired power generation system in China," Energy Policy, Elsevier, vol. 117(C), pages 358-369.
    15. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    16. Tang, Miaohan & Hong, Jingke & Liu, Guiwen & Shen, Geoffrey Qiping, 2019. "Exploring energy flows embodied in China's economy from the regional and sectoral perspectives via combination of multi-regional input–output analysis and a complex network approach," Energy, Elsevier, vol. 170(C), pages 1191-1201.
    17. Guan, Shihui & Han, Mengyao & Wu, Xiaofang & Guan, ChengHe & Zhang, Bo, 2019. "Exploring energy-water-land nexus in national supply chains: China 2012," Energy, Elsevier, vol. 185(C), pages 1225-1234.
    18. Schleich, Joachim & Faure, Corinne & Meissner, Thomas, 2021. "Adoption of retrofit measures among homeowners in EU countries: The effects of access to capital and debt aversion," Energy Policy, Elsevier, vol. 149(C).
    19. Gulshan Maqbool & Zulqarnain Haider, 2021. "The Impact of Individual Behavior on Household Energy Saving," Journal of Economic Impact, Science Impact Publishers, vol. 3(1), pages 39-46.
    20. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(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:224:y:2018:i:c:p:538-549. 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.