Abstract :One of the most important indicators showing the economic and social development of a country is electricity energy consumption. Most economies based on industry, digital technologies or services are increasingly dependent on electrical energy as an energy source. To provide sufficient and high-quality electrical energy, the demand for electrical energy must be predicted and investments for production must be made accordingly. The demand for electrical energy in Turkey continues to rise in parallel with the increasing population, industrialization, imports, exports, and prosperity. Many studies have been carried out in the literature to estimate the electrical energy demand. In this study, the energy demand to be consumed per capita was estimated using Particle Swarm Optimization and Linear Regression Methods. Population, imports, exports, and gross domestic product are used as based on input values from 1980 to 2019 to analyze these methods. Particle Swarm Optimization and Linear Regression Methods are among the methods frequently used in the literature. The results obtained because of both Particle Swarm Optimization and Linear Regression Methods were analyzed using statistical errors such as MSE, RMSE and MAE. The estimation method developed in this study is a base method for both medium-sized energy providers and industrial operators providing renewable energy sources. Keywords : Electricity Consumption Forecasting, Particle Swarm Optimization, Linear Regression Method
