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Çağatay Berke BOZLAK, Claudia Fernanda YAŞAR
ANALYZING THE IMPACT OF NATURAL GAS PRICES ON DAY-AHEAD ELECTRICITY PRICE FORECASTING IN TURKEY: A COMPARATIVE STUDY OF LSTM AND CNN-LSTM MODELS
 
The accuracy of electricity price forecasting is critical in ensuring the reliability and efficiency of power industry operations. Accurate forecasting can be accomplished by employing advanced artificial intelligence models, such as LSTM and CNN-LSTM, which are known for their strong analysis capabilities in time series data, allowing the discovery of hidden patterns. The primary goal of this research is to evaluate the performance of the CNN-LSTM model, which uses historical electricity and natural gas prices to perform predictive analysis. This study emphasizes the importance of natural gas prices in the Turkish electricity market as an exogenous variable when forecasting electricity prices in a country where natural gas accounts for 22.91% of total energy generation. The EXIST Transparency Platform provided 5-year historical data, which included hourly records for electricity prices and daily data for natural gas prices. This historical data is used to forecast electricity prices seven days in advance. Multiple measures of model accuracy were used, including mean absolute error, root mean squared error, mean absolute percentage error, and forecast accuracy in line with the correct trends. The results clearly demonstrated the superiority of the CNN-LSTM model over the LSTM models. When the exogenous variable was included, the CNN-LSTM model performed the best, with accurate forecasted trends of electricity prices for six out of seven days. ORCID NO: 0000-0002-2172-7298

Anahtar Kelimeler: Electricity price forecasting, Natural gas prices, LSTM model, CNN model, Prediction algorithms, TensorFlow.



 


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