Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System

Abstract

Making forecasts for the development of a given process over time, which depends on many factors, is in some cases a difficult task. The choice of appropriate methods—mathematical, statistical, or artificial intelligence methods—is also not obvious, given their great variety. This paper presented a model of a forecasting system by comparing the errors in the use of time series on the one hand, and artificial neural networks on the other. The model aims at multifactor predictions based on forecast data on significant factors, which were obtained by automated testing of different methods and selection of the methods with the highest accuracy. Successful experiments were conducted to forecast energy consumption in Bulgaria, including for household consumption; industry consumption, the public sector and services; and total final energy consumption.