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24 | 02 | 2020
10.14489/vkit.2014.05.pp.038-043

DOI: 10.14489/vkit.2014.05.pp.038-043

Кульцова М. Б., Садовникова Н. П., Хржановская О. А., Ушаков А. Ф.
ПОДХОД К ПРОГНОЗИРОВАНИЮ ЭЛЕКТРОПОТРЕБЛЕНИЯ В ЖИЛИЩНОМ СЕКТОРЕ НА ОСНОВЕ КАЧЕСТВЕННОГО И ИМИТАЦИОННОГО МОДЕЛИРОВАНИЯ
(с. 38 – 43)

Аннотация. Проведен комплексный анализ области энергопотребления в жилищном секторе стран Евросоюза, выделены ключевые факторы, влияющие на потребление энергии странами в целом, предложены качественная и имитационная модели для прогнозирования энергопотребления. Анализ результатов моделирования позволил выявить динамические закономерности изменения ключевых компонентов энергопотребления, а также получить их количественные значения. Проведен ретроспективный анализ, подтверждающий адекватность построенных моделей.

Ключевые слова: имитационное моделирование; качественное моделирование; энергоэффективность; энергопотребление.

 

Kultsova M. B., Sadovnikova N. P., Khrzhanovskaya O. A., Ushakov A. F.
AN APPROACH TO PREDICTION OF ENERGY CONSUMPTION IN THE HOUSING SECTOR ON THE BASIS OF QUALITATIVE AND SIMULATION MODELING
(pp. 38 – 43)

Abstract. Energy efficiency increase and energy conservation are the main priorities of urban areas development which has long gone beyond the commercial and industrial sectors to the state level.  The authors had carried out complex analysis of the field of energy consumption in the residential sector of the EU countries. As a result the following three key factors influencing the energy consumption by countries were determined: energy efficiency of equipment; population size; the cost of electricity per kWh. Based on this analysis the authors had developed a qualitative model of energy consumption in the system of qualitative modeling GARP3. This model allows generating the sets of scenarios which include all possible changes in the dynamics of key indicators. Also the model allows revealing the direct and indirect mutual influences of modeled parameters. The next step after identifying the dynamics of the key factors on the qualitative level was to obtain quantitative values of factors over the predicted period. In order to solve this problem the authors proposed to use simulation modeling. Simulation modeling is one of the main tools in decision support systems, it gives an opportunity to evaluate a large number of alternative decisions, and to simulate system behavior for different input data. Applied to the considered domain the simulation model allows solving the following problems: prediction of the total energy consumption in the residential sector, the annual energy consumption per capita and the cost of electricity per kWh. The main classes of energy equipment which contribute to energy consumption in the residential sector were identified: refrigerators and freezers, washing machines, dishwashers, driers, room air-conditioners, electric storage water heater, electric ovens, electric hobs, consumer electronics and other equipment stand-by, lighting, TV-on mode, office equipment, residential electric heating, central heating circulation pumps, miscellaneous. Simulation model of energy consumption on the basis of data from Reports of the European Commission DG Joint Research Center for 2012 for Euro-27 countries was developed using simulation software Vensim 5.0. The retrospective analysis on the basis of historical data for the period 2004 – 2008 was performed by means of the developed model. Average deviation of the simulation results from the actual statistic data of energy consumption for the prediction period does not exceed 3%. The deviation tends to increase every year owing to the accumulation of prediction error. The developed models are planned to be used for decision making support in development of energy systems of urban areas.

Keywords: Simulation modeling; Qualitative modeling; Energy Efficiency; Energy consumption.

Рус

М. Б. Кульцова, Н. П. Садовникова, О. А. Хржановская, А. Ф. Ушаков (Волгоградский государственный технический университет) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Eng

M. B. Kultsova, N. P. Sadovnikova, O. A. Khrzhanovskaya, A. F. Ushakov (Volgograd State Technical University) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

1. Sadovnikova N. P. Quality Management of Urban Environment using the System Dynamics Method // Modern Scientific Research and their Practical Application [Электронный ресурс] // e-journal. 2013. № 4. V. J11307/May. URL: http://www.sworld.com.ua/ index.php/ru/e-journal/the-content-of-journal/j113/18461-j11307 (дата обращения: 07.10.2013).
2. Bertoldi P. Electricity Consumption and Efficiency Trends in the enlarged European Union. Status Report 2006. European Commission DG Joint Research Centre, 2006. P 6.
3. Bertoldi P. Electricity Consumption and Efficiency Trends in the enlarged European Union. Status Report 2009. European Commission DG Joint Research Centre, 2009. P. 95.
4. Bertoldi P. Energy Efficiency Status Report 2012. European Commission DG Joint Research Centre, 2012. P. 143.
5. Bredeweg B., Linnebank F., Bouwer A., Liem J. Garp3: Workbench for qualitative Modelling and Simulation // Ecological Informatics. 2009. № 4. P. 263 – 281.
6. Vensim Product Center [Электронный ресурс]: URL: http://www.vensim.com/ (дата обращения: 07.07.2013).

Eng

1. Sadovnikova N. P. (2013). Quality Management of Urban Environment Using the System Dynamics Method. Modern scientific research and their practical application, J11307(4). Available at: http: //www.sworld. com.ua/index.php/ru/e-journal/the-content-of-journal/j113/ 18461-j11307.
2. Bertoldi P. (2006). Electricity consumption and efficiency trends in the enlarged European Union. Status Report 2006. European Commission DG Joint Research Centre, p. 6.
3. Bertoldi P. (2009). Electricity consumption and efficiency trends in the enlarged European Union. Status Report 2009. European Commission DG Joint Research Centre, p. 95.
4. Bertoldi P. (2012). Energy efficiency status report 2012. European Commission DG Joint Research Centre, p. 143.
5. Bredeweg B., Linnebank F., Bouwer A., Liem J. (2009). Garp3: Workbench for qualitative modelling and simulation. Ecological Informatics, (4), pp. 263-281. doi: 10.1016/j.ecoinf.2009.09.009
6. Vensim Product Center. Available at: http://www.vensim.com/ (Accessed: 07.07.2013).

Рус

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