Neuro-fuzzy models for operational forecasting of electric energy consumption of the urban system

Abstract

The article presents neuro-fuzzy models of the ANFIS (Adaptive Neuro-Fuzzy Inference System) type for operational forecasting of electric energy consumption of the urban system. ANFIS allows adjusting adaptively to the features of time series (TS), automatically take into account dynamically changing trend and seasonal components of TS, as well as solving the problem of their non-stationarity. The article deals with the construction and use of neuro-fuzzy models such as ANFIS (Adaptive Neuro-Fuzzy Inference System) for the operational forecasting of electricity consumption. The proposed ANFIS-models allow adaptive adjustment to the peculiarities of specific TS in the process of its forecasting, automatically take into account the dynamically changing trend and the seasonal component of TS within the “sliding window”, allowing, under conditions of uncertainty and incompleteness of TS, to solve the problem of its non-stationarity for operational forecasting electrical energy. A method for training ANFIS for operational forecasting of electric energy consumption is described. Experimental studies of the use of ANFIS for predicting electric energy consumption in one of the Russian regions were conducted, according to which it was possible to increase the accuracy of the forecast in comparison with neural network models.