The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods

Mehmet Kayakuş

doi:10.17093/alphanumeric.756651

Abstract

Energy demand and consumption are very important for the development and progress of countries. Energy demand is increasing rapidly day by day, especially in developing countries. Energy policies should be determined correctly to sustain the industry sector and make the right investments. Forecasting energy demand in the near and long term is important for the strategy that countries will follow. In this study, by using the monthly electricity energy data realized in Turkey between January 2016 and March 2020 and other data affecting this, a model to estimate electrical energy consumption was developed. In this model, artificial neural networks (ANN) and support vector regression (SVR) were used as methods. This study used 15 independent variables as the input value, and Turkey's energy consumption value as the dependent variable was estimated. Correlation, coefficient of determination, MAE, MSE, RMSE, MAPE statistical methods were used to measure success and error rate, and both models were found to have acceptable error values and success estimation rates. According to the results, it was concluded that the ANN method was more successful than the SVR method.

Highlights

Countries have to consume more energy to continue their progress and to reach the status of developed countries
It was concluded that the artificial neural networks (ANN) method was more successful than the support vector regression (SVR) method
Data on hydraulic, imported coal, hard coal, lignite, natural gas, sun, wind, geothermal, biomass, asphaltite, fuel oil, electricity imports, Turkey's temperature average, Turkey's monthly population, the number of days worked, the number of vacation days were used as the independent variable

Summary

Introduction

Countries have to consume more energy to continue their progress and to reach the status of developed countries. Forecasting energy demand is very important in terms of making sustainable and correct investments. Accurate planning will both provide economic benefits and reduce dependence on other countries. Energy demands were estimated using time series (Özden & Öztürk, 2018), gray prediction (Akay & Atak, 2007), regression model (Karaca & Karacan, 2016; Yüzük, 2019), ANFIS and ARMA models (Bayramoğlu et al, 2017; Demirel, Kakilli, & Tektaş, 2010), genetic algorithm (Yiğit, 2011), fuzzy logic, hybrid models (Çınar, 2007), support vector regression and artificial neural network forecasting techniques.(Es, Kalender, & Hamzaçebi, 2014). In order to estimate the electricity consumption in Turkey, a model consisting of 15 independent variables was designed. The data such as hydraulic, imported coal, hard coal, lignite, natural gas, sun, wind, geothermal, biomass, asphaltite, fuel oil, electricity imports, Turkey's temperature average, Turkey's monthly population, the number of days worked, the number of vacation days between 2016-2020 were used as the independent variable

Methods

Application

Findings

Conclusion