Investigation Of Factors Affecting Demand For Electricity Consumption With Multiple Regression Method

In order to be presented to consumers at reasonable prices, electricity consumption should be predicted before it is generated. This prediction gained more importance with the enactment of the Electricity Market Law No. 4628 and 6446, which liberalized the electricity market. There are many data analysis methods for the prediction of demand. Some of these models are Artificial Neural Networks, Autoregressive Moving Average and Simple/Multiple Regression. Electricity demand forecast for the success of the program has been developed and tested with a variety of methods used in the study data. studies on this issue for smart grids, which is building the network of the future are important. In this study, an electricity demand forecast program is developed by applying regression model, which uses the past data for deriving a conclusion. In addition, simple regression and multiple regressions demand forecasts are presented while investigating the effects of some factors (Gross Domestic National Product, Average Life Expectancy, and Internet Usage) on electricity consumption.