Authors:
Alexey L. Rutskov,Viktor L. Burkovsky,Evgeny V. Sidorenko,Valery N. Krysanov,DOI NO:
https://doi.org/10.26782/jmcms.spl.8/2020.04.00019Keywords:
Smart Objects, distributed objects,PID controllers,fuzzy neural network,fuzzy neural controller,mathematical modeling,Abstract
The implementation of ‘Smart Objects’ is an important part of the development of adaptive Smart Grid structures. For this class of objects, poorly for malizable factors, such as microclimate parameters, environmental indicators, and consumer load, acquire a significant influence. To solve this, PID controllers are usually used in Smart Objects; however, their accuracy is limited. Fuzzy neural controllers are an alternative solution for the integrated optimization of Smart Objects. This article proposes a scalable model of Smart Object equilibrium by the example of basic utility systems (heating, air conditioning/ventilation and illumination). It was found that the use of fuzzy neural controllers in such systems makes it possible to improve their efficiency by increasing the accuracy of energy consumption forecasts. Control systems based on PID controllers and fuzzy neural controllers in Smart Object were comparted only to find that the latter have a higher accuracy.Refference:
I. Alanis, A. Y., Ricalde, L. J., Simetti, C., Odone, F. (2013). Neural model with particle swarm optimization kalman learning for forecasting in smart grids. Mathematical Problems in Engineering, 2013, 1–9.
II. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2016). Realizatsiyaprogrammnogokompleksaprognozirovaniyaurovnyaregionalnogoenergopotrebleniya [Sales program complex: Prediction of the regional level of energy consumption]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 12(3), 41–47.
III. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L., Shukur, O.M. (2016). Realizatsiyaelementovprogrammnogokompleksaprognozirovaniyaregionalnogoenergopotrebleniyanabazeneyronnoyseti [Implementation of elements of program complex for prediction of the regional level of energy consumption based on neural network]. Proceedings of the 4th International Conference on modern methods of applied mathematics, control theory and computer technology (PMUKT-2016), Voronezh, Russia, 59–63.
IV. Çevik, H.H., Çunkaş, M. (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367.
V. Cheng, Z., Juncheng, T. (2015). Adaptive combination forecasting model for china’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646.
VI. Danilov, A.D., Shukur, O.M., Rutskov, A.L. (2016). Analizprimeneniyanechotkikhneyronnykhseteydlyaprognozirovaniyaenergopotrebleniyapromyshlennykhpredpriyatiy [Analysis of the use of fuzzy neural networks for predicting power consumption of industrial enterprises]. Aktualnyyenauchnyyeissledovaniya XXI veka: teoriya i praktika = Topical scientific research of the 21st century: Theory and practice, 4, 6(26), 59–63.
VII. Gusev, K.Yu.,Burkovsky, V.L. (2012). Neyrosetevoyemodelirovaniyedinamikinelineynykhsistem [Neural network modeling of nonlinear dynamics]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 8(12.1), 51–56.
VIII. Hopfield, J.J. (1982). Neural networks and physical systems with emergent computations abilities. Proceedings of the National Academy of Sciences, 79, 2544–2558.
IX. Jiang, X., Ling, H., Yan, J., Li, B., & Li, Z. (2013). Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization. Mathematical Problems in Engineering, 2013.
X. Komartsova, L.G. (2004). Neyrokompyutery [Neurocomputers], 2nd ed. Moscow, Bauman Moscow State Technical University, 400 p.
XI. Krysanov, V.N., Gamburg, K.S., Rutskov, A.L. (2014). Problemyprognozirovaniyapotrebleniyaelektroenergiinapredpriyatiyakh s odnostavochnymtarifom [Problems of forecasting electricity consumption in enterprises having straight-line rate]. Proceedings of International Scientific and Technical Conference “Power Supply Industry and Electrical Engineering”, International Institute of Computers Technology, May 15–21, Voronezh, Russia.
XII. Krysanov, V.N., Rutskov, A.L. (2013). Matematicheskoyemodelirovaniyesistemupravleniyaraspredelonnoynasosnoynagruzki s primeneniyemiskusstvennykhneyronnykhsetey [Mathematical modeling of distributed pump load control systems using artificial neural networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, May 14–15, Voronezh, Russia.
XIII. Krysanov, V.N., Rutskov, A.L. (2014). Primeneniyemetodovneyronnykh i neyro-nechotkikhsetey v system akhupravleniyastaticheskimipreobrazovatelyami v elektroprivode [Application of methods of neural and neuro-fuzzy networks in static converter control systems in electric drive]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.
XIV. Krysanov, V.N., Rutskov, A.L. (2014). Prognozirovaniyepotrebleniyaelektroenergiipromyshlennymipredpriyatiyami s ispolzovaniyemmetodoviskusstvennykhneyronnykh i neyro-nechotkikhsetey [Forecasting power consumption by industrial enterprises using artificial neural and neuro-fuzzy networks]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.
XV. Krysanov, V.N., Rutskov, A.L., Sharapov, Yu.V. (2015). Prostranstvennyy 3-d interpolyator s ispolzovaniyemnechotkoylogiki [Spatial 3-d interpolator with fuzzy logic]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.
XVI. Krysanov, V.N., Rutskov, A.L., Shukur, O., Shukur M. (2015). Prognozirovaniyepotrebleniyaelektroenergii v razvivayushcheysyaregionalnoysistemeelektrosnabzheniya [Forecasting of electricity consumption in a developing regional power supply system]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.
XVII. Krysanov, V.N., Shukur, O., Shukur M., Rutskov, A.L. (2015). Otsenkaeffektivnostiprimeneniyaiskusstvennykhneyronnykh i neyro-nechotkikhseteydlyakontseptsii Smart Grid v elementakhtransporta i potrebleniyaelektroenergii [Evaluation of the effectiveness of use of artificial neural and neuro-fuzzy networks for the Smart Grid concept in the elements of transport and electricity consumption]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.
XVIII. Li, P., Li, Y., Xiong, Q., Chai, Y., Zhang, Y. (2014). Application of a hybrid quantized elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 55, 749–759.
XIX. Mamdani, E.H. (1977). Advances in the linguistic synthesis of fuzzy controllers. IEEE Trans. on Computer, C-26, 1182–1191.
XX. Monteleoni, C., Schmidt, G.A., Saroha, S., Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.
XXI. Nedellec, R., Cugliari, J., Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.
XXII. Panklib, K., Prakasvudhisarn, C., Khummongkol, D. (2015). Electricity consumption forecasting in Thailand using an artificial neural network and multiple linear regression. Energy Sources, Part B: Economics, Planning, and Policy, 10(4), 427–434.
XXIII. Pierrot, A., Goude, Y. (2011). Short-term electricity load forecasting with generalized additive models. Proceedings of ISAP Power, 593–600.
XXIV. Rutskov, A.L., Gagarinov, N.V., Romanov, A.V. (2015). Analizeffektivnostiupravleniyarezhimamisetey 220 kV [Efficiency analysis of mode control in 220 kV networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.
XXV. Rutskov, A.L., Myazin, D.S., Romanov, A.V. (2015). Povysheniyetekhnologicheskoy i energeticheskoyeffektivnostinaklonnykhdiffuzionnykhustanovokputomoptimizatsiiparametrov s primeneniyemneyro-nechotkikhprintsipov [Improving the technological and energy efficiency of inclined diffusion plants by optimizing their parameters using neuro-fuzzy principles]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.
XXVI. Shi B., Yu-Xia L I., Xin-Hua Y.U. (2009). Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model. Journal of Computer Applications, 29(4), 1036–1039.
XXVII. Zadeh, L.A. (1974). Outline to a new approach to the analysis complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 3, 28–44.
View | Download