Forecasting Market Clearing Prices in Electricity Markets with Time Series Based Machine Learning Models

The Turkish Electricity Market has experienced various procedural transformations over time. These changes have led to the establishment of a system in the electricity market that allows stakeholders to secure hourly energy through next-day sales and purchases. This system is known as the pre-day market and the price set within this framework is referred to as the market clearing price. This study was designed to predict the electricity price for the next 24 time units within the next 24 h in Turkey. Predictions of the market clearing price were conducted using numerous machine learning models. Time series data of clearing prices in Turkey were used in the analysis. Exogenous variables such as production amount and holiday dummies were also incorporated. The data period was from January 2021 to December 2023. The study utilized two lagged market clearing price features with 19 independent lagged and unlagged additional variables. Various machine learning models were tested for their efficacy in forecasting the market clearing price, to identify the most effective one. To benefit from the various advantages of different models, the three models with the best performance, lightGBM, OMP, and STLF were blended to obtain a new model.