Abstract
In this paper, examined is the problem of forecasting monthly total electric energy consumption of multiple heat supply stations based on the data of a Moscow heating supply company. To increase the accuracy of forecasting energy consumption for heat supply stations it is proposed to use artificial neural networks. The features of the proposed neural-network forecasting model include historical data of energy consumption, average monthly outside air temperature and average monthly relative humidity as meteorological variables. The intelligent system for forecasting monthly total energy consumption of heat supply stations includes ANN-based forecasting subsystems with predictor units that allow to produce several forecast variants that can be combined. The knowledge base helps a forecasting specialist to select the most rational of the forecasts.
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