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Energy efficiency-based course timetabling for university buildings

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  • Song, Kwonsik
  • Kim, Sooyoung
  • Park, Moonseo
  • Lee, Hyun-Soo

Abstract

With increasing concern for energy savings in universities, operational solutions, such as control strategies and occupant interventions, have been recommended to reduce energy use, due to the limited responsibilities of faculties and students for energy saving. Additionally, the energy-efficient allocation of classrooms can contribute to achieving further energy saving, because they have different spatial and functional capacities, which result in variation in energy use. In this context, course timetabling can be regarded as a basic source of allocating specific classrooms to lectures. However, there have been few attempts to consider spatial and functional capacities related to energy use in classrooms. Further, little is known about investigating the impact of course timetabling on energy consumption in classrooms. Therefore, this research proposes an energy efficiency-based course timetabling algorithm that identifies an optimal timetable in terms of energy use. The experimental results using the proposed algorithms make it evident that the optimal timetable produces 4% energy saving during cooling and heating season compared to the existing timetable. In addition, more energy savings up to 5.0% are achieved by mitigating hard constraints. The developed algorithm allows for administration staff and facility managers to schedule a course timetable that achieves energy savings in university buildings.

Suggested Citation

  • Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    • Handle: RePEc:eee:energy:v:139:y:2017:i:c:p:394-405
    DOI: 10.1016/j.energy.2017.07.176
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    References listed on IDEAS

    as
    1. Burke, Edmund Kieran & Petrovic, Sanja, 2002. "Recent research directions in automated timetabling," European Journal of Operational Research, Elsevier, vol. 140(2), pages 266-280, July.
    2. Anderson, Kyle & Song, Kwonsik & Lee, SangHyun & Krupka, Erin & Lee, Hyunsoo & Park, Moonseo, 2017. "Longitudinal analysis of normative energy use feedback on dormitory occupants," Applied Energy, Elsevier, vol. 189(C), pages 623-639.
    3. Pongcharoen, P. & Promtet, W. & Yenradee, P. & Hicks, C., 2008. "Stochastic Optimisation Timetabling Tool for university course scheduling," International Journal of Production Economics, Elsevier, vol. 112(2), pages 903-918, April.
    4. Thepphakorn, Thatchai & Pongcharoen, Pupong & Hicks, Chris, 2014. "An ant colony based timetabling tool," International Journal of Production Economics, Elsevier, vol. 149(C), pages 131-144.
    5. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    6. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    7. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    8. Papoulias, D. B., 1980. "The assignment-to-days problem in a school time-table, a heuristic approach," European Journal of Operational Research, Elsevier, vol. 4(1), pages 31-41, January.
    9. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
    10. Sabar, Nasser R. & Ayob, Masri & Kendall, Graham & Qu, Rong, 2012. "A honey-bee mating optimization algorithm for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 216(3), pages 533-543.
    11. de Gans, Onno B., 1981. "A computer timetabling system for secondary schools in the Netherlands," European Journal of Operational Research, Elsevier, vol. 7(2), pages 175-182, June.
    12. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    13. Carrasco, M. P. & Pato, M. V., 2004. "A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem," European Journal of Operational Research, Elsevier, vol. 153(1), pages 65-79, February.
    14. Yang, Zheng & Ghahramani, Ali & Becerik-Gerber, Burcin, 2016. "Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency," Energy, Elsevier, vol. 109(C), pages 641-649.
    15. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
    16. Chwieduk, Dorota, 2003. "Towards sustainable-energy buildings," Applied Energy, Elsevier, vol. 76(1-3), pages 211-217, September.
    17. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
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