Нейросетевое прогнозирование полной приведенной стоимости электроэнергии для солнечных фотоэлектрических систем

Abstract

Elaboration of a new approach to the development of models for predicting the economic indicators of solar photovoltaic systems by using artificial neural network algorithms is becoming of special importance. As is known, the relationships between economic indicators are often difficult to identify. Nonlinear autoregressive models can provide more reliable results than those obtained from predictive linear models based on vector autoregression. The article presents the results from the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar photovoltaic systems based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with hidden sigmoid neurons and linear output neurons has been developed. The input layer is made up of the following variables: the amount of power consumed from solar photovoltaic systems around the world; the total worldwide energy consumption; domestic consumption of energy, gas, coal, and lignite; the shares of renewable energy, wind and solar energy in electricity generation; carbon dioxide emissions from fuel combustion; the price of Brent oil in US dollars, and the average price for natural gas. The output layer determines the LCOE values for solar photovoltaic systems. The developed NARX network was trained on the basis of retrospective data for 2005-2010 using the Levenberg-Marquardt algorithm. The correlation coefficient value achieved in the course of training made 0.99904, and the mean square error value was in the range from 0.00042 to 0.0029