ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES

Abstract

Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.