(untitled) (Third Sector Social Economic Review, 2025)
The complex structure of energy markets and the predictability of prices in these markets hold great significance for both economic stability and sustainable development goals. Particularly in countries like Turkey, where energy demand is rapidly increasing, accurately forecasting the Market Clearing Price (MCP) enables market participants to make effective decisions and enhances the transparency of energy markets. This study comparatively analyzes the effectiveness of six different machine learning algorithms (Linear Regression, Decision Tree, Support Vector Machines, Gaussian Process Regression, Kernel Ridge Regression, and Neural Networks) for forecasting MCP in Turkey’s energy market. Using energy market data from January 2012 to January 2023, the impacts of factors such as temperature, gross electricity consumption, total installed capacity, exchange rates, and Brent oil prices on MCP were examined. The results indicate that the Gaussian Process Regression (GPR) and Decision Tree (DT) models provide higher accuracy in MCP prediction compared to other methods. The GPR model demonstrated consistent performance in both training and testing phases, achieving the lowest RMSE and the highest R² values. Moreover, the DT model delivered stable results during the testing phase, ranking as the second-best performing model.