HYBRID APPROACH TO THE FORECASTING OF ELECTRIC CONSUMPTION TIME SERIES FOR ORGANIZATIONAL MANAGEMENT IN THE WHOLESALE MARKET

Abstract

The problem of increasing the efficiency of solving the complex of tasks of forecasting and planning electric consumption by regional companies of electricity suppliers subjects of the organizational management system in the wholesale electricity market is considered. The analysis of the use of various modeling methods in solving the problem of choosing and building a model for forecasting electric consumption is carried out. The task of constructing a hybrid prognostic model devoid of the shortcomings of individual modeling methods is formulated. Preference is given to the approach associated with the integrated use of mathematical tools based on apparatus of artificial neural networks, a genetic algorithm and a Kalman filter for constructing generalized nonlinear multifactor models. It will increase the efficiency of the model building process and their subsequent use for searching both short-term and long-term forecasts. In order to eliminate the effect of random components of the time series with an uneven distribution of the values of the electric consumption on the training process of the neural network as a non-linear forecasting model, we suggest its preliminary preparation using the Kalman filter. Further optimization of the neural network topology is carried out on the basis of a genetic algorithm that allows, at the mutation stage, to adaptively choose the type of structure transformation most suitable for a given network configuration. Ref. 24, pic. 3

Keywords

• hybrid approach
• time series forecasting
• electric consumption processes
• organizational management
• wholesale electricity market