Energy-Based University Timetabling Model as a Function of Class, Climate, and Building Conditions Subject to Educational Constraints

Document Type : Original Article


1 Molla-Sadra St , Zand Blvd

2 Department of Ocean Operations and Civil Engineering, NTNU, Ålesund, Norway.

3 Department of ICT and Natural Science, NTNU, Ålesund, Norway.

4 Department of Renewable Energy and Environment, Faculty of New Sciences and Technologies, University of Tehran

5 Department of Energy Engineering, Sharif University of Technology, Tehran, Iran



This study investigates the effects of ACH, students’ number, and wall thickness, as well as different semester starting dates and energy consumption reduction. The optimal academic timetabling for reducing energy consumption considers curricula’s rules for taking courses, departments’ specific instructions, existing classes, professors’ priorities, and other related factors. This research uses simulation and demand-side management models to determine the energy consumption of holding classes during a timeslot. They can quantify the factors’ effects on energy use. ACH is between 1.5 and 12, wall thickness is up to 1.6 of its normal value, and students are 10 to 40. There are three starting dates for the semester: conventional time, one-week and two-week earlier. As long as there is no need to change cooling/heating systems, the factors’ impacts on each timeslot from the energy reduction perspective when implementing optimal timetabling are investigated. The developed model revealed that the four factors do not change classes’ priorities from the energy viewpoint but noticeably affect energy use reduction. The optimal scheduling by keeping the semester’s starting date and classes’ operational conditions decreases energy consumption between 11.5 and 24.5 %. The results show that the semester’s early start has a substantial influence on energy consumption reduction in way that if the operational conditions are the same and classes begin two weeks earlier, energy consumption will be reduced between these two ranges: 36.7 - 52.2 % and 49.4 - 63.9 %.


Main Subjects

1. L. Cozzi, T. Gould, S. Bouckart, D. Crow, T.-Y. Kim, C. McGlade, P. Olejarnik, B. Wanner, and D. Wetzel, “World energy outlook 2020,” International Energy Agency: Paris, France, pp. 1–461, 2020.
2. S. S. Ghouri, “World energy outlook-2050: Policy options,” 2007.
3. A. Albatayneh, D. Alterman, A. Page, and B. Moghtaderi, “The impact of the thermal comfort models on the prediction of building energy consumption,” Sustainability, vol. 10, no. 10, p. 3609, 2018.
4. L. Pérez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy and buildings, vol. 40, no. 3, pp. 394–398, 2008.
5. E. A. O. Batlle, J. C. E. Palacio, E. E. S. Lora, A. M. M. Reyes, M. M. Moreno, and M. B. Morejón, “A methodology to estimate baseline energy use and quantify savings in electrical energy consumption in higher education institution buildings: Case study, federal university of itajubá (unifei),” Journal of Cleaner Production, vol. 244, p. 118551, 2020.
6. M. S. Gul and S. Patidar, “Understanding the energy consumption and occupancy of a multi-purpose academic building,” Energy and Buildings, vol. 87, pp. 155–165, 2015.
7. A. Enshassi, A. Ayash, and S. Mohamed, “Key barriers to the implementation of energy-management strategies in building construction projects,” International Journal of Building Pathology and Adaptation, vol. 36, no. 1, pp. 15–40, 2018.
8. R. Bisset, “Buildings can play a key role in combating climate change,” Bulletin on Energy Efficiency, vol. 7, no. 4, 2007.
9. B. Ó. Gallachóir, M. Keane, E. Morrissey, and J. O’Donnell, “Using indicators to profile energy consumption and to inform energy policy in a university—a case study in ireland,” Energy and Buildings, vol. 39, no. 8, pp. 913–922, 2007.
10. E. Azar and C. C. Menassa, “A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings,” Energy and buildings, vol. 55, pp. 841–853, 2012.
11. M. H. Chung and E. K. Rhee, “Potential opportunities for energy conservation in existing buildings on university campus: A field survey in korea,” Energy and Buildings, vol. 78, pp. 176–182, 2014.
12. E. Azar and C. C. Menassa, “Agent-based modeling of occupants and their impact on energy use in commercial buildings,” Journal of Computing in Civil Engineering, vol. 26, no. 4, pp. 506–518, 2012.
13. A. Emery and C. Kippenhan, “A long term study of residential home heating consumption and the effect of occupant behavior on homes in the pacific northwest constructed according to improved thermal standards,” Energy, vol. 31, no. 5, pp. 677–693, 2006.
14. A. Meier, “Operating buildings during temporary electricity shortages,” Energy and Buildings, vol. 38, no. 11, pp. 1296–1301, 2006.
15. R. Lewis, “A survey of metaheuristic-based techniques for university timetabling problems,” OR spectrum, vol. 30, no. 1, pp. 167–190, 2008.
16. V. A. Bardadym, “Computer-aided school and university timetabling: The new wave,” in international conference on the practice and theory of automated timetabling, pp. 22–45, Springer, 1995.
17. M. Ayob and G. Jaradat, “Hybrid ant colony systems for course timetabling problems,” in 2009 2nd Conference on Data Mining and Optimization, pp. 120–126, IEEE, 2009.
18. P. De Causmaecker, P. Demeester, and G. V. Berghe, “A decomposed metaheuristic approach for a real-world university timetabling problem,”European Journal of Operational Research, vol. 195, no. 1, pp. 307–318, 2009.
19. M. Lindahl, A. J. Mason, T. Stidsen, and M. Sørensen, “A strategic view of university timetabling,” European Journal of Operational Research, vol. 266, no. 1, pp. 35–45, 2018.
20. M. Lindahl, T. Stidsen, and M. Sørensen, “Quality recovering of university timetables,” European Journal of Operational Research, vol. 276, no. 2, pp. 422–435, 2019.
21. S. Daskalaki, T. Birbas, and E. Housos, “An integer programming formulation for a case study in university timetabling,” European journal of operational research, vol. 153, no. 1, pp. 117–135, 2004.
22. M. Mühlenthaler and M. Mühlenthaler, Fairness in academic course timetabling. Springer, 2015.
23. M. Khoshbakht, Z. Gou, and K. Dupre, “Energy use characteristics and benchmarking for higher education buildings,” Energy and Buildings, vol. 164, pp. 61–76, 2018.
24. M. Bourdeau, X. Guo, and E. Nefzaoui, “Buildings energy consumption generation gap: A post-occupancy assessment in a case study of three higher education buildings,” Energy and Buildings, vol. 159, pp. 600–611, 2018.
25. H. N. Rafsanjani, C. R. Ahn, and J. Chen, “Linking building energy consumption with occupants’ energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (niolm),” Energy and buildings, vol. 172, pp. 317–327, 2018.
26. X. Gui, Z. Gou, and F. Zhang, “The relationship between energy use and space use of higher educational buildings in subtropical australia,” Energy and Buildings, vol. 211, p. 109799, 2020.
27. A. Al-Daraiseh, N. Shah, and E. El-Qawasmeh, “An intelligent energy management system for educational buildings,” International Journal of Distributed Sensor Networks, vol. 9, no. 9, p. 209803, 2013.
28. M. F. Haniff, H. Selamat, R. Yusof, S. Buyamin, and F. S. Ismail, “Review of hvac scheduling techniques for buildings towards energy-efficient and cost-effective operations,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 94–103, 2013.
29. A. Capozzoli, M. S. Piscitelli, A. Gorrino, I. Ballarini, and V. Corrado, “Data analytics for occupancy pattern learning to reduce the energy consumption of hvac systems in office buildings,” Sustainable cities and society, vol. 35, pp. 191–208, 2017.
30. S. Acharya, A. Shil, C. Debbarma, J. Reang, R. Chakraborty, and A. Ghosh, “Analysis of energy consumption, emission and saving opportunities in an educational institute in northeast india,” International Journal of Energy and Water Resources, vol. 4, pp. 375–388, 2020.
31. B. Vand, K. Martin, J. Jokisalo, R. Kosonen, and A. Hast, “Demand response potential of district heating and ventilation in an educational office building,” Science and Technology for the Built Environment, vol. 26, no. 3, pp. 304–319, 2020.
32. Z. Yang and B. Becerik-Gerber, “The coupled effects of personalized occupancy profile based hvac schedules and room reassignment on building energy use,” Energy and Buildings, vol. 78, pp. 113–122, 2014.
33. K. Sethanan, S. Theerakulpisut, and C. Benjapiyaporn, “Improving energy efficiency by classroom scheduling: a case study in a thai university,” Advanced Materials Research, vol. 931, pp. 1089–1095, 2014.
34. B. Lim, M. Van Den Briel, S. Thiébaux, S. Backhaus, and R. Bent, “Hvac-aware occupancy scheduling,” in Proceedings of the AAAI conference on artificial intelligence, vol. 29, 2015.
35. T. Jafarinejad, A. Erfani, A. Fathi, and M. B. Shafii, “Bi-level energy efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving,” Sustainable Cities and Society, vol. 48, p. 101539, 2019.
36. J. Kim, T. Hong, J. Jeong, M. Lee, M. Lee, K. Jeong, C. Koo, and J. Jeong, “Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region,” Applied Energy, vol. 204, pp. 1431–1443, 2017.
37. Y. Sun, X. Luo, and X. Liu, “Optimization of a university timetable considering building energy efficiency: An approach based on the building controls virtual test bed platform using a genetic algorithm,” Journal of Building Engineering, vol. 35, p. 102095, 2021.
38. J. Yeo, Y. Wang, A. K. An, and L. Zhang, “Estimation of energy efficiency for educational buildings in hong kong,” Journal of Cleaner Production, vol. 235, pp. 453–460, 2019.
39. A. Fathi, M. Salehi, M. Mohammadi, Y. Rahimof, and P. Hajialigol, “Cooling/heating load management in educational buildings through course scheduling,” Journal of Building Engineering, vol. 41, p. 102405, 2021.
40. J. Kim, T. Hong, J. Jeong, C. Koo, and M. Kong, “An integrated psychological response score of the occupants based on their activities and the indoor environmental quality condition changes,” Building and Environment, vol. 123, pp. 66–77, 2017.
41. H. Yoshino, T. Hong, and N. Nord, “Iea ebc annex 53: Total energy use in buildings—analysis and evaluation methods,” Energy and Buildings, vol. 152, pp. 124–136, 2017.
42. S. Chen, G. Zhang, X. Xia, Y. Chen, S. Setunge, and L. Shi, “The impacts of occupant behavior on building energy consumption: A review,” Sustainable Energy Technologies and Assessments, vol. 45, p. 101212, 2021.
43. V. Garg, J. Mathur, and A. Bhatia, Building energy simulation: A work book using designbuilder™. CRC Press, 2020.
44. P. S. Charlesworth, “Air exchange rate and airtightness measurement techniques-an applications guide.,” 1988.
45. N. H. Abu-Hamdeh, R. A. Alsulami, and R. I. Hatamleh, “A case study in the field of building sustainability energy: Performance enhancement of solar air heater equipped with pcm: A trade-off between energy consumption and absorbed energy,” Journal of Building Engineering, vol. 48, p. 103903, 2022.
46. F. Ascione, N. Bianco, G. M. Mauro, and D. F. Napolitano, “Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. application to different italian climatic zones,” Energy, vol. 174, pp. 359–374, 2019.
47. X. Meng, Y. Huang, Y. Cao, Y. Gao, C. Hou, L. Zhang, and Q. Shen, “Optimization of the wall thermal insulation characteristics based on the intermittent heating operation,” Case studies in construction materials, vol. 9, p. e00188, 2018.
48. J. Kocí, V. Ko ˇ cí, J. Mad ˇ era, and R. ˇ Cern ˇ y, “Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data,” Renewable and Sustainable Energy Reviews, vol. 100, pp. 22–32, 2019.
49. B. Chen, Q. Liu, H. Chen, L. Wang, T. Deng, L. Zhang, and X. Wu, “Multiobjective optimization of building energy consumption based on bim-db and lssvm-nsga-ii,” Journal of Cleaner Production, vol. 294, p. 126153, 2021.
50. W. A. Friess and K. Rakhshan, “A review of passive envelope measures for improved building energy efficiency in the uae,” Renewable and Sustainable Energy Reviews, vol. 72, pp. 485–496, 2017.
51. V. Harish and A. Kumar, “A review on modeling and simulation of building energy systems,” Renewable and sustainable energy reviews, vol. 56, pp. 1272–1292, 2016.
52. C. Deb, F. Zhang, J. Yang, S. E. Lee, and K. W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renewable and Sustainable Energy Reviews, vol. 74, pp. 902–924, 2017.
53. M. Krarti, Energy audit of building systems: an engineering approach. CRC press, 2020.
54. A. Escobedo, S. Briceño, H. Juárez, D. Castillo, M. Imaz, and C. Sheinbaum, “Energy consumption and ghg emission scenarios of a university campus in mexico,” Energy for sustainable development, vol. 18, pp. 49–57, 2014.
55. T. A. Nguyen and M. Aiello, “Energy intelligent buildings based on user activity: A survey,” Energy and buildings, vol. 56, pp. 244–257, 2013.
56. C. Peng and Z. Wu, “In situ measuring and evaluating the thermal resistance of building construction,” Energy and Buildings, vol. 40, no. 11, pp. 2076–2082, 2008.
57. K. Gowri, D. W. Winiarski, and R. E. Jarnagin, “Infiltration modeling guidelines for commercial building energy analysis,” tech. rep., Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2009.
58. M. H. Sherman, “Infiltration-pressurization correlation: Simplified physical modeling,” 1980.
59. M. J. Sorgato, A. P. Melo, and R. Lamberts, “The effect of window opening ventilation control on residential building energy consumption,” Energy and Buildings, vol. 133, pp. 1–13, 2016.
60. A. Handbook-Fundamentals, “American society of heating,” Refrigerating and Air-Conditioning Engineers, 2009.
61. C. C. C. A. C. Company, Handbook of air conditioning system design, vol. 1. McGraw-Hill Companies, 1965.
62. S. Edition, “Ashrae handbook,” Stephen Comstock: Atlanta, GA, USA, 1993.
63. G. Roshan, M. Moghbel, and S. Attia, “Evaluating the wind cooling potential on outdoor thermal comfort in selected iranian climate types,” Journal of Thermal Biology, vol. 92, p. 102660, 2020.
64. X. Gui, Z. Gou, and Y. Lu, “Reducing university energy use beyond energy retrofitting: The academic calendar impacts,” Energy and Buildings, vol. 231, p. 110647, 2021.