Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed Algorithm

• Published in: Advances in Electrical and Computer Engineering Vol. 20; no. 2; pp. 27 - 34
• Main Authors: BESKIRLI, A., TEMURTAS, H., OZDEMIR, D.
• Format: Journal Article
• Language: English
• Published: Suceava Stefan cel Mare University of Suceava 01.05.2020
• Subjects: Algorithms; Ant colony optimization; demand forecasting; Economic forecasting; Electricity; Energy consumption; Energy industries; Energy industry; energy optimization; Energy resources; Evolutionary algorithms; Evolutionary computation; Forecasts and trends; GDP; Genetic algorithms; Gross Domestic Product; Heuristic; heuristic algorithms; Industrial development; Methods; Neural networks; Nuclear power plants; Optimization algorithms; Optimization techniques; Optimization theory; Particle swarm optimization; Planning; Regression analysis; Researchers; Seeds; Social change; Studies; China; Turkey
• ISSN: 1582-7445; 1844-7600; 1844-7600
• DOI: 10.4316/AECE.2020.02004

Energy plays an important role in every stage of human life in different forms and variations. Along with the developments in economic, social and industrial fields, the amount of energy that countries need is increasing day by day. Therefore, it is significant to estimate the energy demand for a country's economic activities accurately. In this study, the energy demand forecast (EDF) application optimization problem of Turkey, one of the real-world optimization problems, was performed by MTSA (Modified Tree Seed Algorithm) and TSA (Tree Seed Algorithm) methods. From 1979 to 2005, gross domestic product (GDP), population, export and import values were used as parameter data. Thus, in the presence of three different possible scenarios, Turkey's energy demand from 2006 to 2025, which was estimated by MTSA and TSA methods. To demonstrate the success of MTSA and TSA in the problem of energy demand forecasting (EDF), they are compared with Ant Colony Algorithm (ACO), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Differential Evolution Algorithm (DEA) and Artificial Algae Algorithm (AAA) methods which are in the literature. According to the results of the analysis, it was observed that the MTSA method was a successful estimation tool for energy demand. Index Terms--algorithms, demand forecasting, energy optimization, heuristic algorithms.