Quantum Inspired Evolutionary Algorithm to Improve the Accuracy of a Neuronal Model of the Electric Power Exchange

摘要

This work intends to improve research on artificial neural network model of the Electric Power Exchange using Date of the Day Ahead Market in MATLAB and Simulink environment using Evolutionary Algorithm inspired by quantum computer science. The developed artificial neural network was initially trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy as the input quantities in each hour of the twenty for hours day, and average prices as output quantities. The obtained model of the exchange system was then improved using the evolutionary algorithm. Lastly, we have proposed further improvement to the accuracy of the model, by supplementing the existing evolutionary algorithm with Quantum Inspired Evolutionary Algorithm as a solutions type of Hybrid Intelligent Systems, such as the initial population, crossover and mutation operators, selection, fitness function etc. The reprint shows how you can build a model of artificial neural network inspired by quantum solutions in Simulink environment, which is the purpose of further research.