STRUCTURE OF DECISION SUPPORT SYSTEM OF INFORMATION SYSTEM INTELLIGENT CLIMATE CONTROL RESIDENTIAL
DOI:
https://doi.org/10.15588/1607-3274-2017-1-19Keywords:
intelligent system, indoor climate, expert system, power consumptionAbstract
Context. The ever-growing tendency to rise in price of energy makes it necessary to reduce power consumption, that is, to save energy.In terms of accommodation, the introduction of microclimate necessary for the organization of comfortable conditions for the subjects and
economical use of energy.
Objective. The purpose of work is to solve the actual problem of energy-efficient indoor climate control based on the use of information
intellectual system which takes into account the wishes of the subjects are there, which in turn, helps to ensure effective management of
heating devices by reducing or increasing the ambient temperature.
Method. The solution of the problem suggested by the use of expert system structure as a component of the intelligent control system of
indoor climate through the use of neuro-fuzzy inference subsystem. This subsystem allows you to automatically generate control information
for indoor climate control, depending on the wishes of the subjects, summarizing information on the time and place of their stay in different
periods of time. As a logical subsystem suggested a five-layer neuro-fuzzy feedforward error system, which implements the fuzzy inference
Sugeno zero order. Scheme of intelligent indoor climate control system is also proposed and the approach to the implementation of the process
of identifying the subjects in the room.
Results. The experimental results confirmed the efficiency of the proposed expert system structure in systems «Smart House». It was also set parameters affecting the quality and performance of the proposed system. As an energy source, natural gas has been elected, and the average temperature ranges premises.
Conclusions. A feature of the proposed system is the versatility of the use of any air conditioning, as well as to automatically adjust the
room climate to meet the wishes of subjects. Also, the main feature of the proposed method is to determine the microclimate settings and
memory behavior of the subjects of the room combined with neural networks makes it possible to predict and detect relevant indoor climate
values, and as a result, to save energy.
References
Конох И. С. Разработка и исследование интеллектуальной системы регулирования параметров микроклимата помещения / И. С. Конох, И. С. Гула, С.В. Сукач // Электромеханические и энергосберегающие системы. – 2010. – №3 (11). – С. 80–85. 2. Мансуров Р. Ш. Экспериментальное исследование переходных процессов в системах обеспечения микроклимата / Р. Ш. Мансуров //Теоретические основы теплогазоснабжения и вентиляции: IV Международная научно-техническая конферен- ция, Москва, 10–12 октября 2011 г. : тезисы докладов. – Москва: МГСУ, 2011. – С. 382–387. 3. Кувшинов Ю. Я. Динамические свойства помещения с регулируемой температурой воздуха / Ю. Я. Кувшинов // Известия вузов. Строительство и архитектура. – 1993. – № 4. – С. 201–210. 4. Управление микроклиматом [Электронный ресурс]. – Режим доступа: http://www.soliton.com.ua/pr/MA-2009-Feb-Produalsmall.pdf. 5. Расчет Гкал на отопление [Электронный ресурс]. – Режим доступа: http://otoplenie-gid.ru/operacii/raschety/364-raschetgkal- na-otoplenie 6. Штовба С. Д. Проектирование нечетких систем средствами MATLAB / С. Д. Штовба. – М. : Горячая линия, 2007. – 288 с. 7. Mayer A. An intelligent system for the climate control and energy savings in agricultural greenhouses / A. Mayer, E. Kamel, F. Enrico // Energy Efficiency. – 2016. – № 9. – P. 1241–1255 8. Marvuglia A. Coupling a neural network temperature predictor and a fuzzy logic controller to improve thermal comfort regulation in an office building / A. Marvugla // Building and Environment. – 2014. – № 72. – P. 287–299. 9. Рутковская Д. А. Нейронные сети, генетические алгоритмы и нечеткие системы / Д. А. Рутковская. – М. : Питер, 2006. – 124 с. 10. Abonyi J. Cluster analysis for data mining and system identification / J. Abonyi, B. Feil. – Basel: Birkh user, 2007. – 303 p.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2017 A. I. Kupin, I. O. Muzyka, D. I. Kuznetsov
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
