Investigation of autoregressive forecasting models for market electricity price

Forecast models of ARIMA-type been investigated in application to market electricity prices behavior prediction in the form of time series. Paper presents the results of the forecast achieved accuracy study for constructing forecast via autoregressive statistical models and their close derivatives. Rather detailed computational procedures presented and supplied with numerical results. Adequacy verification of forecast mathematical models with reference to historical natural data in the form of time series carried out with reference to numerical estimation of the standard error. The achieved accuracy level of the designed predictive models for electrical energy market were found through Belgorod region in European part of Russian coincides with published results over international energy markets in Europe, America and Australia. Comparative analysis and interpretation of mathematical models for prediction, both published and obtained in this work leads to the conclusion that increasing complexity of statistical autoregressive forecast models (complexity of structures, the number of unknown parameters, the combination of heterogeneous components, the introduction of correction coefficients) only in individual cases and slightly increases the prediction accuracy. It is concluded that essential step effect of the forecast accuracy can be obtain through composed modeling of dependent variable with reference to the most influenced factors, and the problem to be solved is the design of the aggregate model structure.