Using connectionist systems for electric energy consumption forecasting in shopping centers

Authors:
V. A. Kamaev;M. V. Shcherbakov;D. P. Panchenko;N. L. Shcherbakova;A. Brebels
Affiliations:
State Technical University, Volgograd, Russia;State Technical University, Volgograd, Russia;State Technical University, Volgograd, Russia;State Technical University, Volgograd, Russia;Katholieke Hogeschool Kempen, Geel, Belgium
Venue:
Automation and Remote Control
Year:
2012

Citing 3
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Abstract

A solution is presented for the short-term electrical energy forecasting in shopping centers located in the Netherlands and Belgium. A forecasting method is proposed on the basis of connectionist systems. General description of the forecasting method is provided, as well as its specific features with respect to the forecasting problem are studied. Several connectionist models are generalized, stated and applied, notably, moving average model, linear regression model, and neural network model. In addition, changes in forecasting quality are demonstrated depending on different input variables. The results of using these connectionist models are discussed, and conclusions regarding specific features of every model are outlined.