CASPIAN JOURNAL
MANAGEMENT AND HIGH TECHNOLOGIES
Data mining in the urban heating scheduling system
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Read | Finogeev Aleksey G., Nefedova Irina S., Finogeev Yegor A., Kuang Vin Tkhay, Kamaev Valeriy A., Shevchenko Sergey V., Finogeev Aleksandr A. Data mining in the urban heating scheduling system // Caspian journal : management and high technologies. — 2014. — №2. — pp. 182-196. |
Finogeev Aleksey G. - D.Sc. (Engineering), Professor, Penza State University, 40 Krasnaya St., Penza, 440026, Russian Federation, finogeev@sura.ru
Nefedova Irina S. - post-graduate student, Penza State University, 40 Krasnaya St., Penza, 440026, Russian Federation, nefedya2008@yandex.ru
Finogeev Yegor A. - post-graduate student, Penza State University, 40 Krasnaya St., Penza, 440026, Russian Federation, nefedya2008@yandex.ru
Kuang Vin Tkhay - Director, Institute of Information Technology, Hanoi, Vietnam, tqvinh@ioit.ac.vn
Kamaev Valeriy A. - D.Sc. (Engineering), Professor, Volgograd State Technical University, 28, Lenin av., Volgograd, 400131, Russian Federation, Vkamaev40@mail.ru
Shevchenko Sergey V. - Ph.D. (Engineering), Associate Professor, National Technical University “Kharkiv Polytechnic Institute”, 21 Frunze St., Kharkiv, 61002, Ukraine, sv_shevchenko@mail.ru
Finogeev Aleksandr A. - Assistant, Penza State University, 40 Krasnaya St., Penza, 440026, Russian Federation, nefedya2008@yandex.ru
In this paper we consider a task of Data mining system development for decisions support during the urban heating services management. We defined tasks of the urban heating system based on modern trends in implementation of new energy efficiency improvement technologies. The analysis of system problems of integrated automated control for the city heat supply is proposed. We defined a task of energy efficiency improvement in respect of costs and energy losses minimization during the generation and transportation of energy to the consumer, energy consumption optimization for a given level of reliability of engineering networks and customer satisfaction. To optimize decision support we proposed an approach to facilities monitoring of utilities and technological processes of energy transportation and consumption by using geospatial analysis technology and Knowledge Discovery in Databases (KDD). Data collecting for analytic tasks is implemented in wireless heterogeneous environment that includes VPN sensor segments, ZigBee technologies that realize 6LoWPAN standard over the IEEE 802.15. standard, and also segments of mobile networks. We used JBoss Application server as a server platform for data extraction workbench, collected by sensor nodes, PLC and Energy metering. KDD was developed using Java Enterprise Edition and Spring and ORM Hibernate technologies. For Big Data structuring we proposed organization of data in the form of gipertablits and acceleration technology of Analytical Processing Graphics Processor Unit using technology Compute Unified Device Architecture.
Key words: интеллектуальный анализ данных, поддержка принятия решений, теплоснабжение, энергоэффективность, энергетика, беспроводная сеть, ZigBee, сенсорная сеть, извлечение знаний, гипертаблица, CUDA, data mining, decision support, heat supply, energy efficiency, p