Developing Prescriptive and Performance Indicators Framework for Energy Efficiency of Office Buildings Using Delphi Method

Articles in Press

Document Type : Research paper

Authors

1 Department of Mechanical Engineering, Imam Hossein Comprehensive University, Tehran, Iran.

2 Department of Electrical Engineering, Engineering Faculty of Khoy, Urmia University of Technology, Urmia, Iran.

Abstract

Optimizing electrical energy consumption in office buildings has become a critical concern for researchers and policymakers with regard to the growing emphasis on energy efficiency and sustainability. For this reason, this paper presents a comprehensive study on prescriptive indicators for electrical energy consumption in office buildings, utilizing the Delphi method to ensure their validity and reliability. The analysis categorizes building energy usage into eight key domains: electric motors, lighting, reactive power, office equipment, smart systems, distributed generation, electricity distribution losses within the building, and overall building performance. For each category, relevant indicators are systematically developed. A structured questionnaire was designed and distributed among experts in the electrical energy field to validate these indicators. Based on their assessments, a total of 19 indicators were established, comprising 17 prescriptive indicators and 2 performance indicators. These indicators were then classified based on their effectiveness into three groups: highly effective, effective, and less effective. According to the experts’ assessments and statistical evaluation, indicators including electric motor efficiency, compliance with load characteristics, adoption of high-efficiency electric motors, RPM control mechanisms, lighting equipment efficiency, lighting system power density, the presence or absence of switching-type lighting control, and the energy labeling of office equipment demonstrated a higher degree of influence on energy consumption. Therefore, given their significant impact, these factors should be prioritized in building energy design and auditing to optimize efficiency and performance.

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References

  1. J. Ji, “Machine learning application in building energy consumption prediction: A comprehensive review,” J. Build. Eng., vol. 104, p. 112295, 2025.
  2. Y. Ou, “The price premium of residential energy performance certificates: A scoping review of the European literature,” Energy Build., vol. 332, p. 115377, 2025.
  3. S. Sadi, J. Gholami, M. Fereydooni, and S. Moshari, “Development of water conservation indicators for office buildings using delphi method,” Jordan J. Mech. Ind. Eng., vol. 16, pp. 247–259, 2022.
  4. S. Saedi, M. Alilou, and J. Gholami, “Electrical energy saving in office equipment: Life cycle cost method,” Iran. J. Energy, vol. 24, pp. 101–134, 2021.
  5. J. M. Rey-Hernández et al., “Assessing the performance of a renewable district heating system to achieve nearly zero-energy status in renovated university campuses: A case study for Spain,” Energy Convers. Manag., vol. 292, p. 117439, 2023.
  6. S. Saedi, J. Gholami, and M. Alilou, “Classification of energy consumption of lighting system in buildings: A new method,” Iran. J. Energy, vol. 24, pp. 7–33, 2021.
  7. International Energy Agency, “Energy efficiency 2023,” tech. rep., International Energy Agency, Paris, France, Oct. 2023.
  8. National Renewable Energy Laboratory, “Demand flexibility in large office buildings: Strategies and impacts,” Tech. Rep. NREL/TP-5500-83552, National Renewable Energy Laboratory, Golden, CO, USA, 2023.
  9. A. C. Menezes, A. Cripps, R. A. Buswell, and D. Bouchlaghem, “Benchmarking small power energy consumption in office buildings in the United Kingdom: A review of data published in CIBSE guide F,” Build. Serv. Eng. Res. Technol., vol. 34, no. 1, pp. 73–86, 2013.
  10. U.S. Environmental Protection Agency, “Energy Star.” http: //www.energystar.gov, 2023.
  11. U.S. Green Building Council, “LEED.” https://new.usgbc.org/ leed, 2024.
  12. National Iranian Standards Organization, Non-Residential Buildings—Energy Saving Scheme and Label Instructions. 2012.
  13. J. Smith, R. Kumar, and H. Al-Mansouri, “Benchmarking HVAC energy performance in commercial buildings: A data-driven approach,” Energy Build., vol. 258, p. 111823, 2022.
  14. S. Lee and D. Park, “Prescriptive lighting efficiency metrics for residential buildings: A simulation-based study,” J. Build. Perform., vol. 14, no. 2, pp. 85–98, 2023.
  15. L. Zhang and Y. Chen, “Comparative analysis of energy efficiency frameworks in office buildings: LEED, BREEAM, and DGNB,” Sustainability, vol. 16, no. 11, p. 4345, 2024.
  16. International Energy Agency, “Energy efficiency 2022.” https://www.iea.org/reports/energy-efficiency-2022, 2022.
  17. National Iranian Standards Organization, Residential Buildings—Energy Saving Scheme and Label Instructions. 2012.
  18. Y. Wang et al., “Hybrid AC/DC microgrid architecture with comprehensive control strategy for energy management of smart building,” Int. J. Electr. Power Energy Syst., vol. 101, pp. 151–161, 2018.
  19. C. Roldán-Blay et al., “Optimal energy management of an academic building with distributed generation and energy storage systems,” in IOP Conf. Ser.: Earth Environ. Sci., IOP Publishing, 2017.
  20. R. Missaoui, H. Joumaa, S. Ploix, and S. Bacha, “Managing energy smart homes according to energy prices: Analysis of a building energy management system,” Energy Build., vol. 71, pp. 155–167, 2014.
  21. M. T. Fahey, “Electrical wiring for buildings,” 2009.
  22. D. Lillis et al., “Smart home energy management,” in Recent Advances in Ambient Intelligence and Context-Aware Computing, pp. 155–168, IGI Global, 2015.
  23. M. Krarti, Energy Audit of Building Systems: An Engineering Approach. CRC Press, 2016.
  24. R. Saidur, “A review on electrical motors energy use and energy savings,” Renew. Sustain. Energy Rev., vol. 14, no. 3, pp. 877–898, 2010.
  25. Measurable Energy, “7 energy efficiency trends in commercial real estate for 2025.” https://www.measurable.energy/blog/ energy-efficiency-commercial-real-estate-trends-2025, 2025. Blog post.
  26. C. M. Burt, X. Piao, F. Gaudi, B. Busch, and N. Taufik, “Electric motor efficiency under variable frequencies and loads,” J. Irrig. Drain. Eng., vol. 134, no. 2, pp. 129–136, 2008.
  27. CEMEP, “European committee of manufacturers of electrical machines and power electronics.” http://www.cemep.org/.
  28. M. Zhang, L. Chen, and Y. Wang, “Neural network-based prediction of energy consumption in office buildings,” Energy Informatics, vol. 7, no. 1, pp. 1–15, 2024.
  29. E. H. L. Mazo, C. M. L. Parra, and F. V. Arroyave, “Standards of energy efficiency of induction motors: Latin American context,” in Proc. SICEL, 2013.
  30. A. Gupta, R. Singh, and M. Alavi, “Smart IoI systems for real-time energy optimization in office buildings,” in Proc. IEEE Int. Conf. Smart Infrastructure and Energy Systems, pp. 112–118, 2025.
  31. International Energy Agency, “Energy efficiency 2022.” https://www.iea.org/reports/energy-efficiency-2022, 2022.
  32. L. Zhang and Y. Chen, “Comparative analysis of energy efficiency frameworks in office buildings: LEED, BREEAM, and DGNB,” Sustainability, vol. 16, no. 11, p. 4345, 2024.
  33. R. Saidur, “Energy consumption, energy savings, and emission analysis in Malaysian office buildings,” Energy Policy, vol. 37, no. 10, pp. 4104–4113, 2009.
  34. Organisation for Economic Co-operation and Development, “Energy efficiency 2022.” https://www.oecd.org/en/ publications/energy-efficiency-2022_679f39bd-en.html, 2022.
  35. P. Boyce and P. Raynham, SLL Lighting Handbook. CIBSE, 2009.
  36. P. Tregenza and D. Loe, The Design of Lighting. Routledge, 2013.
  37. D. L. DiLaura et al., The Lighting Handbook: Reference and Application. Illuminating Engineering Society, 2011.
  38. K. Kawamoto, Y. Shimoda, and M. Mizuno, “Energy saving potential of office equipment power management,” Energy Build., vol. 36, no. 9, pp. 915–923, 2004.
  39. M. H. Hosni and B. T. Beck, “Update to measurements of office equipment heat gain data,” Tech. Rep. 1482-RP, ASHRAE, 2011.
  40. Electrical and Mechanical Services Department, Guide for Energy Efficient Green Office Equipment. Hong Kong: Hong Kong SAR Government, 2011.
  41. European Union Directorate-General for Energy, “Labeling energy efficient office equipment.” http://www.eu-energystar. org/.
  42. P. H. Shaikh et al., “A review on optimized control systems for building energy and comfort management of smart sustainable buildings,” Renew. Sustain. Energy Rev., vol. 34, pp. 409–429, 2014.
  43. R. Harper, Inside the Smart Home. Springer, 2006.
  44. T. S. Ustun, C. Ozansoy, and A. Zayegh, “Recent developments in microgrids and example cases around the world—A review,” Renew. Sustain. Energy Rev., vol. 15, no. 8, pp. 4030–4041, 2011.
  45. A. Mondal, S. Misra, and M. S. Obaidat, “Distributed home energy management system with storage in smart grid using game theory,” IEEE Syst. J., vol. 11, no. 3, pp. 1857–1866, 2017.
  46. A. Parisio, M. Molinari, D. Varagnolo, and K. H. Johansson, “Energy management systems for intelligent buildings in smart grids,” in Intelligent Building Control Systems, pp. 253–291, Springer, 2018.
  47. E. A. Drost, “Validity and reliability in social science research,” Educ. Res. Perspect., vol. 38, no. 1.
  48. P. R. Pintrich, D. A. Smith, T. Garcia, and W. J. McKeachie, “Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ),” Educ. Psychol. Meas., vol. 53, no. 3, pp. 801–813, 1993.
  49. H. A. DeVon, M. E. Block, P. Moyle-Wright, D. M. Ernst, S. J. Hayden, D. J. Lazzara, S. M. Savoy, and E. Kostas-Polston, “A psychometric toolbox for testing validity and reliability,” J. Nurs. Scholarsh., vol. 39, no. 2, pp. 155–164, 2007.
  50. C. H. Lawshe, “A quantitative approach to content validity,” Pers. Psychol., vol. 28, no. 4, pp. 563–575, 1975.
  51. P. D. Leedy and J. E. Ormrod, Practical Research. Pearson Custom, 2005.
  52. J. E. Helms, K. T. Henze, T. L. Sass, and V. A. Mifsud, “Treating Cronbach’s alpha reliability coefficients as data in counseling research,” Couns. Psychol., vol. 34, no. 5, pp. 630–660, 2006.
  53. J. A. Gliem and R. R. Gliem, “Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales,” in Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, 2003.
  54. G. A. Churchill and D. Iacobucci, Marketing Research: Methodological Foundations. Dryden Press, 2006.
  55. H. Li, T. Hong, S. H. Lee, and M. Sofos, “System-level key performance indicators for building performance evaluation,” Energy Build., vol. 209, p. 109703, Feb. 2020.
  56. D. F. Polit and C. T. Beck, “The content validity index: Are you sure you know what’s being reported?,” Res. Nurs. Health, vol. 29, pp. 489–497, Oct. 2006.
Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 30 July 2026

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History

  • Receive Date: 17 July 2025
  • Revise Date: 18 October 2025
  • Accept Date: 09 November 2025
  • First Publish Date: 30 July 2026

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