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Forecasting electric power consumption of technocenosis objects on the basis of values from transformed vector rank distribution
Author(s) -
Oleg Kivchun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1901/1/012071
Subject(s) - rank (graph theory) , basis (linear algebra) , electric power , computer science , resource (disambiguation) , resource distribution , energy consumption , object (grammar) , power (physics) , mathematics , resource allocation , artificial intelligence , geometry , physics , electrical engineering , engineering , combinatorics , computer network , quantum mechanics
The article examines a technique for predicting the electric power consumption of technocenosis objects on the basis of the values of the transformed vector rank distribution. This technique includes the following stages: preparing data on electric power consumption, calculating an additional resource for technocenosis objects, forming transformed rank distribution and forecasting its values. The theoretical basis of the methodology was formed by the tenets of the theory of vector rank analysis, the methodology of functional rank analysis and the theory of mathematical statistics, as well as the concepts “technocenosis”, “MS-cenosis” and “bifurcation”. Its distinctive feature is the possibility to take into account the external control effect of a higher power system-MC-cenosis by adding or deducting an additional resource calculated using the system of transformed vector rank distributions. The additional resource is the difference between the values of the technocenosis located on the vector rank distribution of the higher MC-cenosis and the lower permissible value of the lower boundary of permissible values. The presented technique can be implemented in software and hardware complexes and situational centers for electric power consumption control in the area, energy system, transport network complex, large infrastructures and enterprises. The results will significantly supplement the methods of resource management for large infrastructure and energy facilities and form a resource consumption plan individually for each object.

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