Citation: Yotov, K.; Hadzhikolev, E.;
Hadzhikoleva, S.; Cheresharov, S.
Neuro-Cybernetic System for
Forecasting Electricity Consumption
in the Bulgarian National Power
System. Sustainability 2022, 14, 11074.
https://doi.org/10.3390/
su141711074
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara Gallardo
Saavedra
Received: 25 July 2022
Accepted: 31 August 2022
Published: 5 September 2022
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Article
Neuro-Cybernetic System for Forecasting Electricity
Consumption in the Bulgarian National Power System
Kostadin Yotov, Emil Hadzhikolev , Stanka Hadzhikoleva * and Stoyan Cheresharov
Faculty of Mathematics and Informatics, University of Plovdiv Paisii Hilendarski, 236 Bulgaria Blvd.,
4027 Plovdiv, Bulgaria
* Correspondence: stankah@uni-plovdiv.bg
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.
Keywords: electricity consumption; forecast energy consumption; forecasting system
1. Introduction
The forecasting of the future is extremely important for the effective management of
a process or system. Forecasting is about predicting the future as accurately as possible,
given all of the information available, including historical data and knowledge of any
future events that might impact the forecasts [
1
]. From a scientific point of view, forecasting
is a scientifically based assumption about the future state and development of processes,
events, indicators, etc. [
2
]. Considering the possibility of the existence of many different
forecasts for the development of a given process in the future, forecasting can be defined
as a reasonable assumption of possible options for development in a given area and the
probability that they will be realized.
The synergy between mathematics and computer science has led to the development
of a wide variety of algorithms, approaches, methods, and tools for forecasting. Widely
used, with application in various fields are mathematical and statistical methods including
regression and clustering [
1
,
3
], time series [
4
,
5
], polynomial approximations [
6
], fuzzy
collaborative methods [
7
], as well as many methods for artificial intelligence predicting,
such as machine learning [
8
,
9
], etc. On the one hand, this diversity provides an opportunity
to choose a specific approach to solving a given task, but, on the other hand, it makes it
difficult to find the most effective solution.
In the process of our work on multifactor and multi-step forecasting of energy con-
sumption in the Republic of Bulgaria, we came to the need to forecast many socio-economic
factors through which to make the final forecast. The functions, by which the individual
factors change, as well as the energy consumption, can have a variety of linear and nonlin-
ear forms, where the appropriate forecasting methods for each of them may be different.
Determining the most accurate forecast values for the factors would have a positive effect
on the accuracy of forecasting the target value, which in our case is energy consumption.
The automation of the process of choosing the most effective method for any individual
factor or target value contributes to the acceleration of the process and the improvement of
Sustainability 2022, 14, 11074. https://doi.org/10.3390/su141711074 https://www.mdpi.com/journal/sustainability
