Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050

Abstract

Particle swarm optimization (PSO) and genetic algorithm (GA) are the most important optimization techniques among various modern heuristic optimization techniques. The study aims to forecast the energy consumption in Turkey until the year 2050 using PSO and GA models. The annual data provided by the Ministry of Energy and Natural Resources, International Energy Agency (IEA), OECD, Turkish Statistical Institute were used in the study. PSO and GA energy demand forecasting models are developed using population, import, export and gross domestic product (GDP). All models are proposed in linear and quadratic forms. Turkey's energy consumption is projected according to four different scenarios. According the analysis results, the study found for the PSO analysis theR^2 values in the linear model was 91.72%, in the quadratic model was 94.06% at the same time for the GA analysis R^2 values in the linear model was 91.71%, in the quadratic model was 93.97%. Additionally, the mean absolute percent error rates were 11.58% for PSO and 11.69% for GA in the quadratic model. According to Lewis, these values showed that models could be used for energy consumption estimation purposes. The study determined that the statistical performance criteria of PSO models were more successful than the statistical performance criteria of GA models.

Keywords

• Particle swarm optimization
• genetic algorithm
• energy consumption

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