Energy Benchmarking Models for Hotel Buildings: A Case Study in Londrina, Paraná, Brazil
DOI:
https://doi.org/10.5433/1679-0375.2024.v45.50380Keywords:
energy benchmarks, energy efficiency, energy indicators, hotelsAbstract
Room occupancy is an important variable influencing electrical energy consumption in hotel buildings. Given the randomness of this variable, energy demands fluctuate, making it challenging to develop accurate models for describing energy use patterns. This research aimed to develop energy benchmarking models for hotels. For this, five hotel buildings in Londrina, Paraná, Brazil, were used as a reference. Data were collected through questionnaires, interviews with hotel managers, site visits, and analysis of project plans. The analyzed indicators included energy use intensity per built-up area (EUI), room night (EUIRN), average room area (EUIRA), and number of rooms (EUIR). Benchmark equations were obtained by multiple linear regression. The response variables were annual energy use, EUI, EUIRA, and EUIR. Benchmarks were classified on a scale from A to E according to operational energy performance, with A being the most efficient and E the least efficient. Of the five buildings, three were categorized into the same class by the three studied models. The classes of the other two buildings varied according to the model, demonstrating the importance of choosing adequate energy performance indicators for each analysis.
Downloads
References
Alzboun, N., Khawaldah, H., Allan, M., & Backman, K. (2017). Exploring the financial leakages in the classified Hotels in Jordan: a managerial perspective. International Journal of Hospitality and Tourism Administration, 18(4), 429–443. DOI: https://doi.org/10.1080/15256480.2017.1289139
Barbetta, P. A., Reis, M. M., & Bornia, A. C. (2010). Estatística para cursos de engenharia e informática (3rd ed.). UFSC.
Barros, M. V. F. B., Barros, O. N., Polidoro, M., & Pereira, A. C. F. (2021). Atlas Digital da Região Metropolitana de Londrina. Universidade Estadual de Londrina. http://www.uel.br/projetos/atlasrml/mapas/rml.html
Boemi, S. N., Slini, T., Papadopoulos, A. M., & Mihalakakou, Y. (2011). A statistical approach to the prediction of the energy performance of hotel stock. International Journal of Ventilation, 10(2), 163–172. DOI: https://doi.org/10.1080/14733315.2011.11683945
Bohdanowicz, P., & Martinac, I. (2007). Determinants and benchmarking of resource consumption in hotels - case study of hilton international and scandic in europe. Energy and Buildings, 39(1), 82–95. DOI: https://doi.org/10.1016/j.enbuild.2006.05.005
Borgstein, E., & Lamberts, R. (2014). Desenvolvimento de benchmarks nacionais de consumo energético de edificações em operação. Conselho Brasileiro de Construção Sustentável. http://www.cbcs.org.br/website/comunicacao-tecnica/show.asp?ppsCode=F7DB9C2C-E746-423C-8759-3922567EE4A5
Buso, T., & Corgnati, S. P. (2017). A customized modelling approach for multi-functional buildings – Application to an Italian Reference Hotel. Applied Energy, 190, 302–1315. DOI: https://doi.org/10.1016/j.apenergy.2017.01.042
Chedwal, R., Mathur, J., Agarwal, G. D., & Dhaka, S. (2015). Energy saving potential through Energy Conservation Building Code and advance energy efficiency measures in hotel buildings of Jaipur City, India. Energy and Buildings, 92, 282–295. DOI: https://doi.org/10.1016/j.enbuild.2015.01.066
Chen, Y., Tan, H., & Berardi, U. (2017). Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach. Energy and Buildings, 148, 228–237. DOI: https://doi.org/10.1016/j.enbuild.2017.05.003
Chung, W. (2011). Review of building energy-use performance benchmarking methodologies. Applied Energy, 88(5), 1470–1479. DOI: https://doi.org/10.1016/j.apenergy.2010.11.022
Conselho Brasileiro de Construção Sustentável. (2021a). RT2A.03 - Relatório de Auditorias Energéticas - Tipologia de Hotel Vertical de Médio e Grande Porte. CBCS, Procel, Eletrobras. https://www.gov.br/mme/pt-br/assuntos/ee/publicacoes-e-estudos/RT2A.03_210718_final.pdf
Conselho Brasileiro de Construção Sustentável. (2021b). RT2B.03 - Desenvolvimento de arquétipo, modelo de simulação, análise de sensibilidade e equações de benchmark para a tipologia de Hotel Vertical de Médio e Grande Porte. CBCS, Procel, Eletrobras. https://www.gov.br/mme/pt-br/assuntos/ee/publicacoes-e-estudos/copy_of_RT2B.03_210718_final.pdf
Cunha, F. O., & Oliveira, A. C. (2020). Benchmarking for realistic nZEB hotel buildings. Journal of Building Engineering, 30, 1–13. DOI: https://doi.org/10.1016/j.jobe.2020.101298
Deng, S. M., & Burnett, J. (2000). A study of energy performance of hotel buildings in Hong Kong. Energy and Buildings, 31(1), 7–12. DOI: https://doi.org/10.1016/S0378-7788(98)00067-X
Ding, Y., & Liu, X. (2020). A comparative analysis of data-driven methods in building energy benchmarking. Energy and Buildings, 209, 1–13. DOI: https://doi.org/10.1016/j.enbuild.2019.109711
Ding, Y., Zhang, Z., Zhang, Q., Lv, W., Yang, Z., & Zhu, N. (2018). Benchmark analysis of electricity consumption for complex campus buildings in China. Applied Thermal Engineering, 131, 428–436. DOI: https://doi.org/10.1016/j.applthermaleng.2017.12.024
Energy and Environment Agency of Arrábida [ENA]. (2020). Guia de boas práticas: conjunto de medidas, informações e boas práticas para uma maior eficiência energética no setor hoteleiro [ENA]. http://www.porumturismosustentavel.pt/db/documentos/1081.1.1.5e31a02401b04.pdf
Geraldi, M. S., & Ghisi, E. (2020). Mapping the energy usage in Brazilian public schools. Energy and Buildings, 224, 1–17. DOI: https://doi.org/10.1016/j.enbuild.2020.110209
Gnecco, V. (2021). Desenvolvimento de um modelo de benchmarking local para as escolas públicas de ensino infantil de Florianópolis [Dissertação, Universidade Federal de Santa Catarina]. Repositório Institucional. https://repositorio.ufsc.br/handle/123456789/229348
Gori, A. M. (2017). Econometria Conceitos e Aplicações. Saint Paul.
Instituto de Desenvolvimento Rural do Paraná. (2019). Temperaturas Médias Históricas na Estação de Londrina - PR. http://www.idrparana.pr.gov.br/system/files/publico/agrometeorologia/medias-historicas/Londrina.pdf
Jota, P. R. da S., de Souza, A. P., & da Silva, V. R. (2017). Energy performance indexes: analysis in public schools in Brazil. Energy Efficiency, 10, 1433–1451. DOI: https://doi.org/10.1007/s12053-017-9530-7
Juaidi, A., Alfaris, F., Montoya, F. G., & Manzano-Agugliaro, F. (2016). Energy benchmarking for shopping centers in Gulf Coast region. Energy Policy, 91, 247–255. DOI: https://doi.org/10.1016/j.enpol.2016.01.012
Katircioglu, S., Saqib, N., & Katircioglu, S. (2020). Estimating the effects of tourism growth on emission pollutants: empirical evidence from a small island, Cyprus. Air Quality, Atmosphere and Health, 13(4), 391–397. DOI: https://doi.org/10.1007/s11869-020-00803-z
Lei nº 13.709, de 14 de agosto de 2018. (2018). Dispõe sobre a Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
Lu, S., Wei, S., Zhang, K., Kong, X., & Wu, W. (2013). Investigation and analysis on the energy consumption of starred hotel buildings in Hainan Province, the tropical region of China. Energy Conversion and Management, 75, 570–580. DOI: https://doi.org/10.1016/j.enconman.2013.07.008
Nguyen, A. T., & Rockwood, D. (2019). Developing an energy benchmarking system for hotel buildings using the statistical method and the simulation-based approach. Journal of Green Building, 14(3), 3–22. DOI: https://doi.org/10.3992/1943-4618.14.3.1
Peel, M. C., Finlayson, B. L., & Mcmahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11, 1633–1644. DOI: https://doi.org/10.5194/hess-11-1633-2007
Prefeitura de Londrina. (2022). Certidão narrativa de lançamento de iptu [Accessed: 2024-09-11]. https://portal.londrina.pr.gov.br/iptu/certidao-narrativa
R Core Team. (2021). A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org
Rosselló-Batle, B., Moià, A., Cladera, A., & Martìnez, V. (2010). Energy use, CO2 emissions and waste throughout the life cycle of a sample of hotels in the Balearic Islands. Energy and Buildings, 42(4), 547–558. DOI: https://doi.org/10.1016/j.enbuild.2009.10.024
Roth, J., & Rajagopal, R. (2018). Benchmarking building energy efficiency using quantile regression. Energy, 152, 866–876. DOI: https://doi.org/10.1016/j.energy.2018.02.108
Tang, M., Fu, X., Cao, H., Shen, Y., Deng, H., & Wu, G. (2016). Energy Performance of Hotel Buildings in Lijiang, China. Sustainability, 8(8), 780. DOI: https://doi.org/10.3390/su8080780
Veloso, A. C. O., Souza, R. V. G., & Santos, F. N. (2020). Energy benchmarking for office building towers in mild temperate climate. Energy and Buildings, 222, 12–14. DOI: https://doi.org/10.1016/j.enbuild.2020.110059
Wang, J. C. (2012). A study on the energy performance of hotel buildings in taiwan. Energy and Buildings, 49, 268–275. DOI: https://doi.org/10.1016/j.enbuild.2012.02.016
Yan, D., O’Brien, W., Hong, T., Feng, X., Gunay, H. B., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264–278. DOI: https://doi.org/10.1016/j.enbuild.2015.08.032
Yao, Z., Zhuang, Z., & Gu, W. (2015). Study on energy use characteristics of hotel buildings in Shanghai. Procedia Engineering, 121, 1977–1982. DOI: https://doi.org/10.1016/j.proeng.2015.09.195
Yuan, Z., Wang, W., Wang, H., & Mizzi, S. (2020). Combination of cuckoo search and wavelet neural network for midterm building energy forecast. Energy, 202, 1–15. DOI: https://doi.org/10.1016/j.energy.2020.117728
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Mariana Rolim Guerra, Rafaela Benan Zara, Thalita Gorban Ferreira Giglio
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Copyright Declaration for articles published in this journal is the author’s right. Since manuscripts are published in an open access Journal, they are free to use, with their own attributions, in educational and non-commercial applications. The Journal has the right to make, in the original document, changes regarding linguistic norms, orthography, and grammar, with the purpose of ensuring the standard norms of the language and the credibility of the Journal. It will, however, respect the writing style of the authors. When necessary, conceptual changes, corrections, or suggestions will be forwarded to the authors. In such cases, the manuscript shall be subjected to a new evaluation after revision. Responsibility for the opinions expressed in the manuscripts lies entirely with the authors.
This journal is licensed with a license Creative Commons Attribution-NonCommercial 4.0 International.
