Cooling/heating load management in educational buildings through course scheduling

Abstract This paper presents a comprehensive educational model considering share resources among several departments bounded to educational limitations for optimizing energy consumption. The thermal model calculates the load based on the ambient temperature to minimize the user’s knowledge of the building’s thermal model. It determines the percentage reduction of the building’s heating load in case of a change in room temperature, a term’s start date, and sharing classes compared to standard curricula. It employs linear Boolean optimization. Class planning is carried out concerning minimizing energy use, students’ preferences, charts, educational constraints, professors’ schedules, classes’ numbers, and shared resources. Optimal timetabling influences on the heating load for Shiraz University’s second engineering building complex are analyzed, used by Mechanical Engineering, Computer Engineering departments, and Auditorium. The effects of three scenarios and two situations on energy consumption are quantified. The results indicate the best curriculum is not affected by the scenarios. A two-week early beginning and a temperature of 23C of classes reduce energy consumption by over 50 %. In deploying the optimal timetabling, the potential of diminishing the heating energy consumption is about 12 % compared with conventional timetabling. Sharing resources do not always lead to a decrease in energy use, but this activity can slide it by 2.5 percent. These results are aligned with commercial software’s outcomes. To tackle the obstacle of the computational load, reducing a matrix’s dimensions is implemented. It cuts down the number of decision variables, unequal and equal constraints from 90.8 %, 99.97 %, and 57.81 %, respectively.

[1]  Thomas R. Stidsen,et al.  A fix-and-optimize matheuristic for university timetabling , 2018, J. Heuristics.

[2]  D A Ahlburg,et al.  Simple versus complex models: evaluation, accuracy, and combining. , 1995, Mathematical population studies.

[3]  Thomas R. Stidsen,et al.  A strategic view of University timetabling , 2018, Eur. J. Oper. Res..

[4]  Taehoon Hong,et al.  Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region , 2017 .

[5]  Ferri P. Hassani,et al.  Warming impact on energy use of HVAC system in buildings of different thermal qualities and in different climates , 2014 .

[6]  Kwonsik Song,et al.  Longitudinal Analysis of Normative Energy Use Feedback on Dormitory Occupants , 2015 .

[7]  Amirhossein Fathi,et al.  Bi-level energy-efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving , 2019, Sustainable Cities and Society.

[8]  Min Hee Chung,et al.  Potential opportunities for energy conservation in existing buildings on university campus: A field survey in Korea , 2014 .

[9]  Willett Kempton,et al.  Comparison groups on bills : Automated, personalized energy information , 2006 .

[10]  Iakovos Michailidis,et al.  Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage , 2016 .

[11]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[12]  Pinar Mert Cuce,et al.  A state of the art review of evaporative cooling systems for building applications , 2016 .

[13]  Francis Allard,et al.  Metamodeling the heating and cooling energy needs and simultaneous building envelope optimization for low energy building design in Morocco , 2015 .

[14]  Thomas R. Stidsen,et al.  Quality recovering of university timetables , 2019, Eur. J. Oper. Res..

[15]  Rhyd Lewis,et al.  A survey of metaheuristic-based techniques for University Timetabling problems , 2007, OR Spectr..

[16]  Iakovos Michailidis,et al.  Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule , 2015 .

[17]  Sture Holmberg,et al.  Energy performance comparison of three innovative HVAC systems for renovation through dynamic simulation , 2014 .

[18]  Yadollah Saboohi,et al.  Extended Energy Return on Investment of multiproduct energy systems , 2020, Energy.

[19]  Sandhya Patidar,et al.  Understanding the energy consumption and occupancy of a multi-purpose academic building , 2015 .

[20]  Qiang Zhang,et al.  Model input selection for building heating load prediction: A case study for an office building in Tianjin , 2018 .

[21]  Edmund K. Burke,et al.  A survey of search methodologies and automated system development for examination timetabling , 2009, J. Sched..

[22]  Eric Wai Ming Lee,et al.  An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .

[23]  H. F. Elattar,et al.  A modified method of calculating the heating load for residential buildings , 2014 .

[24]  Andrew Kusiak,et al.  Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms , 2015 .