Comparative Analysis of Machine and Deep Learning Methods in Estimating the Turkish Electricity Market Clearing Price

Abstract

The estimation of the clearing price in the electricity market holds significant strategic importance within the energy sector. Energy firms can enhance their operational efficiency by providing clients with more dependable price alternatives through precise estimation of the market clearing price. The precise determination of the market clearing price holds significant significance in facilitating strategic decision-making for decision makers and investors operating within the energy sector. Accurate pricing projections are crucial for ensuring stability in the energy market and enhancing energy reliability for consumers. Hence, it is imperative to employ novel methodologies and enhance the precision of predictions within the energy sector in order to ascertain precise price estimates. This study utilized hourly power data derived from various sources such as natural gas, dam, lignite, imported coal, wind, solar, geothermal, and biomass. Additionally, hourly electricity demand data was employed as input variables to estimate the clearing price of the electricity market. The study encompasses a total of 8772 hours of data collected between April 17, 2023, to April 16, 2023. The study employed linear regression, XGBoost, Random Forest, LSTM, and SVR techniques for prediction. The models were evaluated by comparing their performances using statistical coefficients such as RMSE, MSE, MAE, and R2. Based on the acquired performance measures, it was noted that the XGBoost approach exhibited the highest level of prediction performance.