IEEE Systems, Man and Cybernetics Magazine - April 2021 - 24

discovery probability and step sizes [32] since the traditional
CS algorithm has a slow convergence rate, and it is easy to fall
into the local optimal solution in the late evolution period. An
improved binary CS algorithm is proposed to solve the unit
commitment problem in an electric power system [33].
Many existing studies on classroom energy conservation
are based on the building itself [12]-[15]. They either ignore
the perspective of students or consider only the arrival of
students. However, when students leave a classroom, this
leads to more available seats. This article proposes an effective scheduling method to solve this problem based on a
simulation model that adopt ICS and LSTM-RNN. Considering student preferences about where they sit, we optimize
the scheduling of powering on/off classrooms based on their
occupancy. The proposed method can save much more electricity on the premise of ensuring students' satisfaction with
the classroom space environment.
Data Collection and Processing
To simulate the student occupation of classrooms, we conducted a questionnaire for 869 students at a university in
Guangdong, China.
The statistical results of the questionnaire are plotted in
Figures 1 and 2, which present the distribution of students'
arrival and departure times in classrooms. Nearly 83.54% of

2.19%

0.69%

13.58%
27.62%

6:31-7:30 p.m.
7:31-8:30 p.m.
8:31-9:30 p.m.
9:31-10:30 p.m.
10:31-11:30 p.m.

55.92%

Figure 1. The arrival time distribution.

0.12%

1.26%

Simulation Model
This article adopts a social force model [34]-[36] to effectively simulate the occupants' seating behavior.
Social Force Model
The walking behaviors of student i are dominated by this
drv
social force, including forward motivation f i , repulsive
forces f ij between students i and j, and force f iw between
student i and obstacle w . The number of obstacles is W.
The social force f i loaded on student i is
	

25.2%
33.49%

6:31-7:30 p.m.
7:31-8:30 p.m.
8:31-9:30 p.m.
9:31-10:30 p.m.
10:31-11:30 p.m.

39.93%

Figure 2. The departure time distribution.
24	

students come to classrooms for their studies between 6:31
and 8:30 p.m., during which about 1.38% of the students
leave the classroom. Between 8:31 and 9:30  p.m., about
13.58% of students come to the classroom, and 33.49% leave
the classroom. Between 9:31 and 11:30 p.m., the proportion
of students entering a classroom is less than 3%, while the
proportion of students leaving a classroom is about 65.13%.
The number of power-on classrooms is mostly determin--
ed by the number of student occupants arriving prior
to 8:30 p.m. It is unnecessary to power on additional classrooms later than 9:30 p.m.
According to the results of 869 replies on this questionnaire and on-site observations, at 8:30 p.m., the number of
student occupants studying in classrooms is likely to reach a
peak. From 8:31 to 9:00 p.m., the number of occupants entering and exiting classrooms keeps a balance. The higher a
floor, the lower its classroom-occupancy rate. Most seat
space requirements in a classroom are also concentrated on
an individual's seat and the one or two adjacent seats on
their left and right sides.
Each semester consists of 18 weeks and can be divided
into three stages: beginning (weeks 1-2, i.e., phase 1), midterm (weeks 3-16, i.e., phase 2), and end (weeks 17-18, i.e.,
phase 3). The occupant flow in the classrooms during evenings is significantly different during the various stages. The
largest number is during phase 3, while the lowest number is
during phase 2, and the number during phase 1 is slightly
higher than that during phase 2. Usually, during phases 1 and
2, the number of occupants in the classrooms is affected by
whether or not it is a holiday, where the number of occupants on holidays is significantly less than that of nonholidays. However, at the end of a semester, the number of
occupants has little to do with whether it is a holiday.

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2021

W

f i = f i + | f ij + | f iw . (1)
drv

w=1

j (≠ i)

drv

The self-driving force f i
	

drv

f i = mi

of student i is expressed as

v ^ t he i ^ t h - v i ^ t h
. (2)
xi
0
i

0

For student i , let m i stand for his/her mass, v i0 (t) for the
0
expected speed, e i (t) for the direction of the desired speed,
v i (t) for his/her current actual speed, and x i for his/her
step size.



IEEE Systems, Man and Cybernetics Magazine - April 2021

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