Archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey

Abstract

Energy consumption is getting rising gradually around the planet. Therefore, the importance of energy management has increased for all nations worldwide, and long-term energy demand estimation is becoming a vital problem for all countries. In this study, linear, quadratic and exponential models based six different Archimedes optimization algorithms (AOA) such as AOA-Linear, AOA-Quadratic, AOA-Exponential, IAOA-Linear, IAOA-Quadratic and IAOA-Exponential have been proposed to make some future projections of Turkey for the years (2021-2050). The previous studies in the literature were used the data set of Turkey, such as observed energy demand (OED), population, gross domestic product (GDP), export and import data for the years (1979-2005) or (1979-2011) obtained from the Turkish Statistical Institute (TUIK) and the Ministry of Energy and Natural Resources (MENR). However, in this study, a new data set is organized with the OED, population, GDP, export and import data of Turkey for the years (1997-2020) to make some long-term energy demand estimations of Turkey, and this dataset is used for the first time in this study. AOA-Linear, AOA-Quadratic and AOA-Exponential algorithms are based on linear, quadratic and exponential mathematical models and the basic AOA method. IAOA-Linear, IAOA-Quadratic and IAOA-Exponential algorithms are also based on linear, quadratic and exponential mathematical models and the improved AOA (For short, IAOA) proposed in this study. Once a sensitivity analysis is made for determining the effect of algorithmic parameters of AOA and IAOA, the proposed algorithms are realized for Turkey's long-term energy demand estimation for the years (2021-2050) with three different future scenarios. According to the experimental results, the quadratic model-based proposed IAOA produces better or comparable performance on the problem dealt with in this study in terms of solution quality and robustness.