Determining the Factors Affecting the Market Clearing Price by Using Multiple Linear Regression Method

The aim of this study is to determine the factors affecting the market clearing price by the multiple linear regression method. In order to achieve this goal, hourly data for 2019 were obtained from the website of Energy Exchange Istanbul (EXIST). Due to the multiple linear regression analysis assumptions, only January and June data were included in the analysis. The analysis results show that the variables affecting the market clearing price are statistically determined as the amount of natural gas production, the amount of hydroelectric energy, the amount of energy produced in thermal power plants, and the amount of wind energy (only in January) at the significance level of 0.05. Methods with high specificity coefficient, low mean absolute percentage error (MAPE) and mean absolute deviation are known as the methods that adapt to the data better. In this study, artificial neural network method was used along with the multiple linear regression method in order to determine which prediction model fit the data better. Coefficient of determination (R2), mean absolute percentage error, and mean absolute deviation were used to compare the methods. In this study, it can be concluded that the artificial neural network method is a better predictive than the multiple linear regression method due to its high R2 and low MAPE and the mean absolute deviation values.