IEEE Systems, Man and Cybernetics Magazine - April 2021 - 33

Table 2. A comparison of the results with
the SD in different situations.
Type

Small classroom

Large classroom

Lecture hall

Occupant Flow

PSO

ABC

ICS

100

13.52

12.88

12.64

200

13.78

13.65

13.72

300

14.29

15.51

14.24

500

17.42

17.33

17.17

1,000

19.39

18.24

18.12

1,500

17.65

18.26

17.23

1,000

28.13

27.82

25.11

2,000

27.49

26.25

23.28

3,000

28.37

26.77

26.06

Conclusion
The ICS algorithm overcomes the shortcomings of the traditional CS, namely that it is easy to be premature and fall
into a local optimum. Compared to the electricity consumption of classrooms without scheduling and with the scheduling of other algorithms for powering them on, ICS
performs the best in different scenarios. ICS provides effective and feasible strategies for the scheduling optimization
of powering on classrooms. After the introduction of LSTMRNN to assist in powering on a classroom, we can avoid
having an additional classroom scheduled to be powered on
when the net inflow of students is fewer than the total number of available popular seats in the previously powered-on
classrooms. Consequently, the electricity consumption can
be further reduced.
In the future, the proposed method will be extended to
schedule the powering on/off of many rooms in a large
library. Deep neural network-based techniques will also be
explored to validate our proposed method.

Jian Zhang (Jianzhzj@163.com) earned his B.S. degree
in computer science from Shaoguan University, Shaoguan,
China. He is currently pursuing his M.S. degree in the School
of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China. His research
interests include intelligent scheduling and optimization.
Yan Hou (houyan@gdut.edu.cn) earned her B.S. and
M.S. degrees in computer science from Yangtze University,
China, in 1999 and 2002, respectively, and her Ph.D. degree
in industrial engineering from the Guangdong University of
Technology, China, in 2016. Since 2002, she has been with
the Guangdong University of Technology, Guangzhou,
510006, China, where she is currently an associate professor.
Her current research interests include production scheduling, information systems, and software engineering.
Yan Qiao (yqiao@must.edu.mo) earned his B.S. and
Ph.D. degrees in industrial engineering and mechanical
engineering from the Guangdong University of Technology, China, in 2009 and 2015, respectively. Since January
2018, he is an assistant professor at the Macau University
of Science and Technology Taipa, Macau, 999078, China.
He was a recipient of the Best Application Paper Award
Finalist of the 2011 IEEE International Conference on
Automation Science and Engineering, the Best Student
Paper Award of the 2012 IEEE International Conference
on Networking, Sensing, and Control, and the Best Conference Paper Award Finalist of the 2016 IEEE International
Conference on Automation Science and Engineering. He is
a Member of IEEE.
References
[1] G. Deconinck and K. Thoelen, " Lessons from 10 years of demand response research:
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Acknowledgments
This work was supported, in part, by the National Natural
Science Foundation of China under grant 61673123 and
grant 61603100, the Natural Science Foundation of Guangdong Province, China, under grant 2020A151501482, and
Key-Area Research and Development Program of Guangdong Province, China, under grant 2020B010166006.

Build., vol. 143, pp. 202-219, May, 2017. doi: 10.1016/j.enbuild.2016.11.028.
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About the Authors
Qing-Hua Zhu (zhuqh@gdut.edu.cn) earned his Ph.D.
degree in industrial engineering from the Guangdong University of Technology, China, in 2013. He joined the Guangdong
University of Technology, Guangzhou, 510006, China, in
2003, and is currently an associate professor with the School
of Computer Science and Technology. His research interests
include scheduling and optimization, discrete event systems,
and Petri nets. He is a Senior Member of IEEE.
	

in nursery and elementary school buildings in the cold climatic zone of Greece, " Energ.
Build., vol. 40, no. 12, pp. 2207-2214, Jan., 2008. doi: 10.1016/j.enbuild.2008.06.011.
[7] H. Ma, J. Lai, C. Li, F. Yang, and Z. Li, " Analysis of school building energy consumption in Tianjin, China, " Energ. Proc., vol. 158, pp. 3476-3481, Feb., 2019. doi: 10.1016/j.
egypro.2019.01.924.
[8] C. D. Jeanfrancois Bonnet, P. Faucher, and J. Roturier, " Analysis of electricity and
water end-uses in university campuses case-study of the University of Bordeaux in the
framework of the Ecocampus European Collaboration, " J. Clean. Prod., vol. 10, no. 1, pp.
13-24, 2002. doi: 10.1016/S0959-6526(01)00018-X.

Ap ri l 2021

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