Abstract
The paper contains selected results of research related to the nature and the implementation of the neural model supported by the evolutionary algorithm inspired by quantum calculations for determination of prices at the Polish Power Exchange. Numeric data quoted at the Day Ahead Market in the period of 1st January 2015 to 30th June 2015 were used to train the artificial neural network in the model of the system. Attention was paid to quantization method, dequantization method and the method of quantum calculations. Significant improvement of the neural model supported by the quantum-inspired evolutionary algorithm was obtained compared with the model without quantum inspiration.
Highlights
The object being modelled is the system of the Polish Power Exchangea (PPE) concerning the concluded transactions quoted on the Day Ahead Market (DAM) with 24 input quantities and 24 output quantities [4,5,78]
I.e. the data related to quotations on the DAM in the period of 01.0130.06.2015 were obtained from the TGE S.A. webpageb
Both the input and the output layer consisted of 24 neurons, with the neurons in the input layer corresponding to the supplied and sold ee in each hour of the 24-hour day, and the neurons in the output layer corresponding to the average weighted prices obtained for the sold ee
Summary
The object being modelled is the system of the Polish Power Exchangea (PPE) concerning the concluded transactions quoted on the Day Ahead Market (DAM) with 24 input quantities and 24 output quantities [4,5,78]. Input quantities are streams of electrical energy (ee) volume supplied and sold at the PPE [MWh] in each hour of the 24-hour day, and output quantities are streams of average prices obtained for sold ee weighted by the volume of ee in each hour of 24-hour day [PLN/MWh]. The neural model was implemented in MATLAB and Simulink environment using Neural Network Toolbox. Both the input and the output layer consisted of 24 neurons, with the neurons in the input layer corresponding to the supplied and sold ee in each hour of the 24-hour day, and the neurons in the output layer corresponding to the average weighted prices obtained for the sold ee. A as a subsystem of polish Towarowa Giełda Energii S.A. (TGE S.A.). b www.tgee.com c perceptron ANN
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