Increase in accuracy forecast for electrical energy consumption of the WEM subjects using fuzzy neural networks

• Published in: IOP conference series. Materials Science and Engineering Vol. 791; no. 1; pp. 12027 - 12034
• Main Authors: Fedorov, Ya P, Burkovsky, V L, Rutskov, A L
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2020
• Subjects: Accuracy; Artificial neural networks; Balancing; Controllers; economic stimuli; electric energy consumption forecast; Electric power systems; Electricity distribution; Energy consumption; Energy sources; Fuzzy control; Fuzzy logic; fuzzy neural networks; Power consumption; simulation modelling; Subsystems; wholesale electricity market
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/791/1/012027

This paper covers in depth functioning essence of the balancing electricity market in the Russian Federation in accordance with the current economic stimuli. Significant role of the prediction process for energy resources consumption has been noted, which has a direct influence on a pricing policy within the marked trade area. Analysis of different factors, that impact execution accuracy of the one day in advance model, was conducted. Problem of the weakly defined and unknown parameters, that influence the performance of the balancing electricity market, is suggested to resolve by means of fuzzy neural controller. The paper presents its structure and settings, and possibility to scale it to use not only as an analyzer but as an executive element in the subsystems of electric power systems. High accuracy performance of the fuzzy neural controller was shown - around (0,5-3) %, that is much higher than methods already in use (for instance, based on a coefficient of decline/growth of power consumption).