A new modified artificial bee colony algorithm for energy demand forecasting problem

The ability to accurately estimate energy consumption in the medium and long term based on actual indications is critical for countries to plan and prioritize their futures and take the appropriate actions. This paper proposes a new modified artificial bee colony (M-ABC) method that can adaptively select an optimal search equation to more accurately estimate Turkey's energy consumption. In the study, linear (M-ABCL) and quadratic (M-ABCQ) mathematical models were developed, and gross domestic product (GDP), population, import, and export data were used as input parameters for energy demand estimation. The weight values in the regression models are calculated according to the objective function with the proposed M-ABC. In this way, the weight values that will produce estimations with the lowest error according to the selected years are found, and then the most appropriate energy demand estimations are made. We compared the performance of our proposed M-ABC algorithm with ant colony optimization (ACO), particle swarm optimization (PSO), and hybrid ACO and PSO (HAP) algorithms. In addition, various estimation suggestions are presented under four different scenarios using input parameters. According to the results, the models suggested with the M-ABC algorithm were more successful in estimating the energy demand. According to the results of the presented four scenarios, the energy demand in 2025 is 145.26, 139.85, 126.26, and 144.17 million tons of oil equivalent (Mtoe) for the M-ABCL model, and 185.62, 161.94, 118.96, and 159.71 Mtoe for the M-ABCQ model, respectively. Thus, it is predicted that average consumption will increase by 51.65% in the linear model and 70.94% in the quadratic model.