https://scientific-jl.com/luch/
Часть-43_ Том-2_ Апрел-2025
168
FUZZY SUMMARISATION SYSTEM RESEARCH
Ismanov Muhammadziyo
Namangan State Technical University, PhD.Dotsent
Akmaljon Yuldashev
Namangan State Technical University, assistant. teacher
Rakhimjanova Dilshoda
Namangan State Technical University, Student of group 34a-23
ABSTRACT: This paper describes the implementation of fuzzy set theory
and Fuz-zy Inference System (FIS) for prediction of electric load. The proposed
technique utilizes fuzzy rules to incorporate historical weather and load data. The
use of fuzzy logic effectively handles the load variations due to special events. The
fuzzy logic has been extensively tested on actu-al data obtained from the Czech
Electric Power Company (ˇCEZ) for 24-hour ahead prediction. Test results
indicate that the fuzzy rule base can produce results better in accuracy than
artificial neural networks (ANNs) method.
Keywords: fuzzy sets, fuzzification, fuzzy logic, fuzzy inference system,
prediction implementation, employees.
Fuzzy logic model has been selected as an alternative method for the load
forecasting problem in this paper. It is a suitable technique in case when the
historical data are not real numbers, but linguistic values. This paper presents the
results of a preliminary investigation of the feasibility of use of a fuzzy logic model
for short-term load forecasting. In this research, historical load and weather data
are converted into fuzzy set theory to produce fuzzy forecasts and defuzzification
is performed to generate a point estimate for system load.
https://scientific-jl.com/luch/
Часть-43_ Том-2_ Апрел-2025
169
DESCRIPTION OF FUZZY INFERENCE SYSTEM. Fuzzy set A is defined
in terms (U, µA), where U is relevant universal set and µ
A
U < 0, 1 > is a
membership function, which assigns each elements from U to fuzzy set A. The
membership of the element x € U of a fuzzy set A is indicated µ
A
(x). We call F (U)
the set of all fuzzy set. Then „classical“ set A is fuzzy set where: µ
A
: U →{0, 1}.
Thus x € A , µ
A
(x) = 0 and x € A , µ
A
(x) = 1 . Let U
i
, i = 1, 2, ..., n, be universals.
Then fuzzy relation R on U = U
1
× U
2
× ... × U
n
is a fuzzy set R on universal U.
Fuzzy Inference System: One of the possible applications is a fuzzy
inference system (FIS) (fuzzy regulator). There are a few types of regulators. We
use the regulator of type P : u = R(e) for our purposes, where the action values
depend only on a regulation deviation:
Input variables: E
i
= (E
i
, T (E
i
), E
i
, G, M), i = 1, ..., n.
Output variables: U = (U, T (U), U, G, M).
We consider the fuzzy regulator as the statement of the type: R = R
1
We do not require the output of fuzzy regulator to be a set in many cases, but
we require the concrete value z
0
€ Z, e.i. we want to make a defuzzification. The
centroid method is the most frequently used method of defuzzification. The FIS
defined in such a way is called Mamdami.
Fig. 1. Block diagram of a fuzzy inference system
DESCRIPTION FIS FOR PREDICTION OF LOAD
The four principal components of a fuzzy system is shown in Figure
https://scientific-jl.com/luch/
Часть-43_ Том-2_ Апрел-2025
170
The fuzzification interface performs a scale mapping that changes the range
of values of input variables into corresponding universe of disco-urse. It also
performs fuzzification that converts nonfuzzy (crisp) input data into suitable
linguistic values, which may be viewed as labels of fuzzy sets. Fuzzy rule base,
which consists of a set of linguistic con-trol rules written in the form “If a set of
conditions are satisfied, Then a set of consequences are inferred”. Fuzzy inference
machine, which is a decision-making logic that employs rules from the fuzzy rule
base to infer fuzzy control actions in response to fuzzified inputs. Defuzzification
interface performs a scale mapping that converts the range of values of output
variables into corresponding universe of discourse. It also per-forms
defuzzification that yields a nonfuzzy (crisp) control action from an inferred fuzzy
control action [2]. A commonly used defuzzification rule known as centroid
method is used here, according to which the defuzzifi-cation interface produces a
crisp output defined as the center of gravity of the distribution of possible actions.
This centroid approach produces a numerical forecast sensitive to all the rules.
Various membership functions have been discussed, and for the particular
application data sets, their effects on model performance have been demonstrated.
The proposed model has been able to generate fore-casts with a MAPE frequently
below 2.8 % for working days, 3.6 % for weekends and 3.4 % for special days. The
simulation results demonstrate the effectiveness of the fuzzy model for 24-hour
ahead prediction.
LITERATURA
1. Mamdani, E.H.: Applications of fuzzy logic to approximate reasoning using
linguistic synthesis, IEEE Transactions on Computers, Vol. 26, No. 12, pp.1182-
1191 (1977)
2. Sugeno, M.: Industrial applications of fuzzy control, Elsevier Science Pub. Co.
(1985)
https://scientific-jl.com/luch/
Часть-43_ Том-2_ Апрел-2025
171
3. Zadeh, L.A.: The concept of a linguistic variable and its application to
approximate reasoning, Parts 1, 2, and 3, Information Sciences, Academic Press,
New York (1975)
4. Zˇ ´AK, L. Fuzzy Objects and Fuzzy Clustering. Proc. 8th Zittau Fuzzy
Colloquium, Zittau, 2000, pp 293 – 302, ISBN 3-00-006723-X.
5. Muhammad Riaz Khan, Libor ˇZ´ak and ˇCestm´ır Ondru°ˇsek, Fuzzy Logic
Based Short Term Electric Load Forecasting, ELEKTRO 2001, 4th Inter- national
Scientific Conference, ˇZilina, 2001, pp 19-25, ISBN 80-7100-836-2
6. Zak, L. Application Of Fuzzy Logic To Time Series. In Aplimat 2005, 4nd
International Conference. Bratislava: FME STU Bratislava, 2005. p. 549-555.
ISBN: 80-969264-1- 1.
7. D. Valiˇs, L. ˇZ´ak and O. Pokora, “Contribution to system failure occurrence
prediction and to system remaining useful life estimation based on oil field data,”
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and
Reliability, vol. 229, no. 1, pp. 36-45, 2015.
8. D. Mazurkiewicz, “Computer-aided maintenance and reliability management
systems for conveyor belts,” Eksploatacja i Niezawodnosc - Maintenance and
Reliability, vol. 16, no.3, pp. 377-382, 2014.
9. D. Mazurkiewicz, “Tests of extendability and strength of adhesive-sealed joints
in the context of developing a computer system for monitoring the condition of belt
joints during conveyor operation,” Eksploatacja i Niezawodnosc - Maintenance
and Reliability, vol. 3, pp. 34-39, 2010.
10. A. Glowacz, “Diagnostics of direct current machine based on analysis of
acoustic signals with the use of symlet wavelet transform and modified classifier
based on words,” Eksploatacja i Niezawodnosc – Maintenance and Reliability, vol.
16, no. 4, pp. 554-558, 2014.
https://scientific-jl.com/luch/
Часть-43_ Том-2_ Апрел-2025
172
11. A. Glowacz, “Diagnostics of synchronous motor based on analysis of acoustic
signals with the use of line spectral frequencies and k-nearest neighbor classifier,”
Archives of Acoustics, vol. 39, no. 2, pp. 189-194, 2014.
12. S. Cornak et al. (2011, November). Lifetime extension of engine oil using
additives. Presented at MendelNet. [online]. Avaiable:
https://mnet.mendelu.cz/mendelnet2011/mendelnet 2011 fulltext.pdf
13. Yuldashev, A. V. (2024). PRINCIPLE OF FORMING AN INTELLECTUAL
MODEL OF DIAGNOSING OBJECT STATES. Economy and Society, (3-2
(118)), 436-440.
14. Valijon o'g, Y. L. A., Saydulla o'g, N. Y., Shavkat o'g, N. S., & Ubaydulla o'g'li,
X. S. (2023). CONSTRUCTION OF NUMERIC CONTROL SYSTEMS USING
FUZZY MODULE. RESEARCH. UZ, 28(5), 31-37.
15. Valijon o‘g, Y. L. A., Shavkat o‘g, J. E., Hakimjon o‘g, S. H., & Farkhod o‘g,
M. F. (2023). KNOWLEDGE REPRESENTATION MODELS IN ARTIFICIAL
INTELLIGENCE. RESEARCH. UZ, 28(5), 22-30.
16. Valijon o‘g, Y. L. A., Davlat o‘g, X. R., & Tirkash o‘g, I. G. A. (2023).
DESIGNING A SUGENO-TYPE SYSTEM USING FUZZY LOGIC. Journal of
new century innovations, 43(2), 97-106.
