Algorithm of the living room illumination
control
Dmitrii VITENBERG, Aleksandr KALIKH, Leonid MYLNIKOV
Perm State Technical University, Komsomolsky Ave 29, Perm, 614990, Russia
Tel: +7 342 2391821, Fax: +7 342 2391822
Abstract: The article covers the process of a room illumination controlling
algorithm development. This algorithm is based on the user's preferences and
calculates the output signal under the influence of external factors. Within the project
there were developed several algorithms such as: the ergonomic database
construction algorithm, the algorithm of data forecast, the correction algorithm.
Keywords: algorithm, light, database, forecast.
1. Introduction
The issue of limited resources in today's world is one of the most pressing. It is
known that stocks of many natural resources are already scarce and even the fact that some
of those resources exist in big amounts doesn't mean that they are unending. It's
necessary to
restrict the
use of
resources, because the
problem
of their
limitation will become insoluble and will lead to fatal consequences in the future. That's
why now the industry of power is being developed rapidly. In particular this development is
being maintained by the intelligent systems of control also known as smart houses.
Algorithms described in this article may be of practical use in systems of this type.
2. Objectives
The aim of our project was to develop the algorithm which maintains the control of
internal illumination of the room. A system based on this algorithm will have qualities such
as self-learning, the ability to predict outcomes and to respond to the disturbing effects. As
a result it will provide the best interaction with the user.
Problems which were solved during the development of the algorithm:
- the ergonomic database construction algorithm
- creation of an algorithm for post processing of experimental data
- selection of appropriate prediction algorithm
- correction of the control signal by taking into account external disturbing influences
3. Methodology and Development
This algorithm was designed with the help of Data Mining methods. The complete
structure of the data analysis system is presented below.
Collection of experimental data
Data systematization
Searching of a model which
describes the available data
Testing of the result model
No
The quality is suitable
Yes
Exploitation
Adding new data
Pic. 1 Structure of the data analysis system
This algorithm was developed on the example of managing the internal illumination of
living room. There is a diagram of the room below.
1 - sensor of outdoor illumination
2 - window
3 - the first illumination source
4 - the second illumination source
5 - sensor of movement
6 - regulator of the first illumination
source
7 - regulator of the second
illumination source
Pic. 2. Diagram of the room
Next goes the development of the algorithm. The first step is to collect
experimental data and organize it into a logical structure. The data will be separated into
two databases. The first database will record directly the values of a light sources
regulator corresponding them to the user's preferences at any time period. The aim of
creation of a second database is to track connections between the user's preferences
and external
disturbing
factors.
These factors may be long periods
of nonstandard for this time of day illumination. For example, it can be an increased cloudiness.
To identify these factors, the current values gained by the sensor of outdoor illumination
will be compared with the statistical data of illumination in this region for the year. In
the case of sufficiently large and prolonged deviations the data will be entered into the
table.
Then the first database should be as follows (Table 1).
Table 1. First database
T
(time moment)
R1
(the value, set by regulator of the
first illumination source)
0:00:00
0:01:00
0:02:00
0:03:00
0:04:00
0:05:00
0:06:00
0:07:00
0:08:00
0:09:00
0:10:00
2
3
3
3
3
4
4
4
4
5
5
R2
(the value, set by regulator of
the second illumination
source)
0
0
1
1
1
1
1
1
2
2
2
However, during the accumulation of data there appears a problem proportional
increasing of database’s size. To solve it, an algorithm of optimizing the database size was
developed. The idea of this algorithm is the following. Data is being entered into the table
at regular intervals. Suppose that for several periods of time a value which is being received
from regulators are similar or differs on minor constituents Ɛ. suppose that for several
periods of time data which is being received from regulators are similar or differs on minor
constituents Ɛ. In order not to construct the huge tables, by recording the same values at
each time, we can write only the first value and to adhere index of the length, which
denotes how many time slots this value will be repeated. Also, the database is divided
into two separate independent databases in order to prevent conflicts arising during the
optimization of the table while simultaneously processing the signals from both regulators.
Start
Read Ri
If
|Ri-1 - Ri|< Ɛ
No
Write Ri
Yes
Write Index
Ri-1 +1
End
Pic 3. Block diagram
After making changes to the database will look as follows:
Table 2. The first database for R1 values
T
(time moment)
R1
(value, set by regulator of the
first illumination source)
0:00:00
0:01:00
0:05:00
0:09:00
2
3
4
5
I1
( index, which shows how many
time slots the value of R1 will
be repeated)
0
4
4
2
Table 3. The first database for R2 values
T
(time moment)
R2
(value set by regulator of the
second illumination source)
0:00:00
0:02:00
0:07:00
0
1
2
The second database will look as follows:
Table 4. The second database
I2
( index, which shows how many
time slots the value of R2 will
be repeated)
2
5
2
R22
(value, set by
regulator of the
second
illumination
source)
L
(value, set by
sensor
of outdoor illumin
ation)
IL
( index, which shows
how many time slots
the value of
illumination will be
repeated)
3
3
6
2
3
1
3
5
4
2
2
4
5
0
1
7
R11
T
( value, set by
(time
regulator of the
moment) first illumination
source)
01.01.11
14:00
02.01.11
8:00
04.01.11
17:00
06.01.11
10:00
Construction of a model based on the obtained data
In order to construct a mathematical model and predict data for a certain period
ahead, the most common method of extrapolation, the least-squares method is selected.
̅̅̅̅̅̅ . Through the
)
As a result of the experiment, we have a set of points (
points the curve must be drawn ( ), which is a linear combination of
̅̅̅̅̅̅̅ .
selected basis functions ( )
( )
((
)
)
- matrix of values of the basis functions at given points,
- matrix of the experimental values.
7
data
6
5
4
data
3
y
2
1
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Checking the model adequacy.
Model gained by extrapolation methods based on sampling data for a certain period
of time, usually helps to predict the data for a period of 2-3 times smaller than a given
period. To test the model efficiency the interval obtained during the prediction should be
compared with the corresponding interval of the last received data. In this case it's
acceptable to use the χ2 criteria.
First of all it is necessary to count the value of variable V:
∑
(
)
– number of points,
– experimental values,
– predicted values.
- number of freedom degrees,
.
To obtain an acceptable value of variable V is necessary to determine it by using
similarity tables. If the number X is chosen from the table, standing on the ν-th row and
column P, then the probability that the value of variable V will be less than or equal to x, is
approximately equal to P. The k value must be sufficiently large. The value of variable V is
compared with the data from the table, where P is probability, and afterwards the
conclusions about the adequacy are made. If the calculated value of variable V based on the
experimental data the than the table value, then the hypothesis is not accepted with
probability P. Some percentage points of χ2 – distribution. If P will take values in
redistribution from 0 to 25%, then the model can be considered adequate.
Table 5. Table for χ2 - method
 1
2
3
4
5
6
7
 8
9
  10
  11
  12
  15
  20
  30
  50
p  1%
p  5%
p  25%
0,00016
0,0201
0,1148
0,2971
0,5543
0,8721
1,239
1,646
2,088
2,558
3,053
3,571
5,229
8,260
14,95
29,71
0,00393
0,1026
0,3518
0,7170
1,1455
1,635
2,167
2,733
3,325
3,940
4,575
5,226
7,261
10,85
18,49
34,76
0,1015
0,5754
1,213
1,923
2,675
3,455
4,255
5,071
5,899
6,737
7,584
8,438
11,04
15,45
24,48
42,94
–2,33
–1,64
  50
xp 
p  50%
p  75%
0,4549
1,323
1,386
2,773
2,366
4,108
3,357
5,385
4,351
6,626
5,348
7,841
6,346
9,037
7,344
10,22
8,343
11,39
9,342
12,55
10,34
13,70
11,34
14,85
14,34
18,25
19,34
23,83
29,34
34,80
49,33
56,33
2 2 2
  2 x p  x p   O ( 1 )

3
3
–0,674
The influence of external factors.
0,00
0,674
p  95%
p  99%
3,841
5,991
7,815
9,488
11,07
12,59
14,07
15,51
16,92
18,31
19,68
21,03
25,00
31,41
43,77
67,50
6,635
9,210
11,34
13,28
15,09
16,81
18,48
20,09
21,67
23,21
24,72
26,22
30,58
37,57
50,89
76,15
1,64
2,33
After confirming the adequacy of the model system is in operation. According to this
predicted value with some assumptions can be taken for real. However, the output will not
always be equal to predict. In cases where the current illumination matches with the
illumination, recorded in a table of external factors for the corresponding time in the past,
the values from the regulators recorded in this table will act as the output value.
4. Results
As a result the algorithm that satisfies all the established objectives was developed.
5. Conclusion
The designed algorithm maintains the control of internal illumination of the room. A
system based on this algorithm will have qualities such as self-learning, the ability to
predict outcomes and to respond to the disturbing effects. Also the algorithm described
in this article may be of practical use in the intelligent control systems also known as smart
houses.
References
[1] Computational methods and tools in automation systems. Tutorial: L. A. Mylnikov, N.
V. Andreevskaya - Perm:PSTU, 2009.
[2] Technologies of data analysis: Data Mining, Visual Mining, Text mining, OLAP/ A. A.
Barsegyan, M.S. Kupriyanov, V. V. Stepanenko, I. I. Holod. – St. Petersburg: BHVPetersburg, 2007.
[3] http://www.basegroup.ru/