Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches

• Published in: Energy sources Vol. 26; no. 14; pp. 1313 - 1320
• Main Authors: ERSEL CANYURT, OLCAY, CEYLAN, HALIM, KEMAL OZTURK, HARUN, HEPBASLI, ARIF
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA Taylor & Francis Group 01.12.2004; Taylor & Francis
• Subjects: Applied sciences; Energy; energy demand; energy modeling; energy planning; Exact sciences and technology; future projections; genetic algorithm; Turkey; Asia; Turkey; Energy consumption; Correlation; Import; Genetic algorithm; Efficiency; Export; Energy demand; Models; Energy policy; Planning; Modeling
• ISSN: 0090-8312; 1521-0510
• DOI: 10.1080/00908310490441610

Energy modeling is a subject of widespread current interest among engineers and scientists concerned with problems of energy production and consumption. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, two forms of the energy demand equations are developed in order to improve energy demand estimation efficiency for future projections based on the genetic algorithm (GA) notion. The genetic algorithm energy demand (GAEDM) model is used to estimate Turkey's future energy demand based on gross domestic product, population, import, and export figures. Both equations proposed here are non-linear, of which one is exponential and the other is quadratic. The quadratic form of the GAEDM model provided a slightly better fit solution to the observed data and can be used with a high correlation coefficient for Turkey's future energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for energy policies.