Affiliations: [a] University of Tyumen, Russia | [b] Northern Trans-Ural state Agricultural University, Russia
Correspondence:
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Corresponding author: Glukhikh Dmitry, University of Tyumen, Russia. Tel.: +7 932 32 000 36; E-mail: [email protected].
Abstract: Smart grid systems are being actively developed and implemented all over the world. However, along with developed systems for monitoring and data analysis, decision support functions are not fully implemented. Wherein decision support is necessary due to the complexity of possible emergencies. In this work, we offer the concept of an intelligent decision support system (IDSS) for the SMART grid, which is based on the hybrid Case-Based Reasoning (CBR) method. This method combines models of knowledge-based systems and models of neural networks and machine learning, which simplifies realization on complex changing objects of the SMART grid. In the first part of the research, we describe the concept of the proposed hybrid-CBR method, the principle of formalizing the situation at the objects of the SMART grid systems and present the involved neural network architecture Comparator-Adder. The second parts of the research reveal the concept of applied IDSS and also show the results of an experiment of retrieving precedent from a knowledge base with using a neural network. Experimental results show that our architecture successfully copes with the task of selecting the most similar situation. We believe that the MAPE error in this incident does not play a key role; the efficiency of the neural network is confirmed primarily by the coherence with the results of the expert choice and the absence of collisions.
Keywords: Case-based reasoning, decision making, decision support system, neural network, neural network architecture, SMART grid, perceptron, comparator, adder, control of complex systems
