System for forecasting energy consumption using the artificial neural network


https://doi.org/10.17073/2500-0632-2016-2-66-77

Full Text:


Abstract

The article considers the possibility of increasing the efficiency of the mining enterprise at the expense of correct choice of price categories and tariff for electricity. The efficiency of forecasting model of energy consumption by the rational choice of price categories is shown, a system for predicting energy consumption using artificial neural network is developed. The forecast error is 0.908 % with the architecture of the 
network type MLP (MLP 24-18-1)


About the Authors

B. N. Abramovich
National Mineral Resources University
Russian Federation

Professor of Department of electric power engineering and electromechanics



I. S. Babanova
National Mineral Resources University
Russian Federation
Department of electric power engineering and electromechanics


References

1. Postanovlenie Pravitel'stva RF «Ob utverzhdenii pravil optovogo rynka jelektricheskoj jenergii i moshhnosti i o vnesenii izmenenij v nekotorye akty Pravitel'stva Rossijskoj Federacii po voprosam organizacii funkcionirovanija optovogo rynka jelektricheskoj jenergii i moshhnosti» [Resolution of the Government of the Russian Federation "On approval of the rules of the wholesale market of electric energy and capacity and on amendments to some acts of the Russian Federation on the issues of functioning of the electric energy and power wholesale market of Government"] (app. 27.12.2010 No. 1172, ed. 29.02.2016).

2. Postanovlenie Pravitel'stva RF «O funkcionirovanii roznichnyh rynkov jelektricheskoj jenergii, polnom i (ili) chastichnom ogranichenii rezhima potreblenija jelektricheskoj jenergii» [Resolution of the Government of the Russian Federation "On the functioning of retail electricity markets, the full and (or) partial restriction of electric power consumption mode"] (app. 04.05.2012 No. 442, ed. 22.02.2016).

3. Babanova I.S. Primenenie iskusstvennyh nejronnyh setej v zadachah rognozirovanija jenergopotreblenija dlja predprijatij mineral'no-syr'evogo kompleksa [Application of artificial neural networks in problems of forecasting energy consumption for businesses mineral complex] // Fundamental'nye i prikladnye issledovanija v sovremennom mire [Fundamental and applied research in the modern world]/ Materialy IX Mezhdunarodnoj nauch.-prakt. konf. [Proc. IX Int. scientificpractical. Conf.] – 2015. – Vol. 1. – pp. 128-134.

4. Abramovich B.N., Babanova I.S. Improvement of monitoring system commercial electricity accounting for compressor plants оn the enterprises for gas industry. Efficiency and sustainability in the mineral industry innovation in Geology, Mining, Processing, Economics, Safety and Environmental Management. Scientific reports on resource issues 2015, TU Bergakademie Freiberg, Value 1, pp. 383-386.

5. Abramovich B.N., Babanova I.S. Avtomatizirovannye sistemy upravlenija jenergopotrebleniem gornyh predprijatij [Automated power management system of mining enterprises]// Materialy XII Mezhdunarodnoj nauchnoj shkoly molodyh uchenyh i specialistov [Proc. XII Int. scientific school for young scientists and specialists], 23-27 November 2015. – M: IPKON RAS, 2015. – pp. 225-229.

6. Abramovich B.N., Babanova I.S. Primenenie iskusstvennyh nejronnyh tehnologij v processe prepodavanija disciplin jelektrotehnicheskogo cikla [Application of artificial neural technologies in teaching electrical cycle disciplines]// Sovremennye obrazovatel'nye tehnologii v prepodavanii estestvenno-nauchnyh i gumanitarnyh disciplin: sbornik nauchnyh trudov II Mezhdunarodnoj nauch.-metod. konf. 09–10 aprelja 2015. [Modern educational technology in the teaching of natural sciences and the humanities: Proc. II Int. scientific-method. Conf. 09-10 April 2015]/ National Mineral Resources University – Sankt-Peterburg, 2015. – pp. 229-234

7. Babanova I.S., Abramovich B.N. Razrabotka perspektivnogo planirovanija jenergosistemy na osnove sozdanija modeli iskusstvennoj nejronnoj seti [Development of long-term power system planning through the creation of an artificial neural network model]// Materialy XI Mezhdunarodnoj nauchnoj shkoly molodyh uchenyh i specialistov [Proc. of the XI Int. scientific school for young scientists and specialists], 24-28 November 2014 – M: IPKON RAS, 2014. – 388 p.

8. Shumilova G.P., Gotman N.Je., Starceva T.B. Prognozirovanie jelektricheskih nagruzok pri operativnom upravlenii jelektrojenergeticheskimi sistemami na osnove nejrosetevyh struktur: ucheb.posobie [Prediction of electrical load in operational control of power systems based on neural network structures: Textbooks] – Ekaterinburg: Ural Branch of RAS, 2008. – 89 p.

9. Weron, Rafal. Modelling and forecasting electricity loads and prices. West Sussex, England: John Wiley & Sons Ltd, 2006.

10. Charytoniuk W., Chen M.S., 2000. Very Short-Term Load Forecasting Using ANN. IEEE Transactions on Power Systems 15 (1), pp. 263-268.

11. Huseynov A.F, Yusifbeyli N.A and Hashimov A.M (2010). «Electrical System Load forecasting with Polynomial Neural Networks (based on Combinatorial Algorithm». Modern Electric Power Systems 2010, Wroclaw, Poland, MEPS’10 - paper 04.3

12. Samsher, K.S. and Unde, M.G., (2012). Short-term forecasting using ANN technique. International Journal of Engineering Sciences and Engineering Technologies, Feb. 2012, ISSN: 2231-6604, Vol. 1, Issue 2, pp: 97-107 © IJSEST

13. Balwant singh Bisht, Rajesh M Holmukhe Electricity load forecasting by artificial neural network model using weather data. International journal of electrical engineering& technology (ijeet) Vol. 4, Issue 1, January-February (2013), pp. 91-99


Supplementary files

For citation: Abramovich B.N., Babanova I.S. System for forecasting energy consumption using the artificial neural network. Gornye nauki i tekhnologii = Mining Science and Technology (Russia). 2016;(2):66-77. https://doi.org/10.17073/2500-0632-2016-2-66-77

Views: 688

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2500-0632 (Online)