An optimized deep learning approach for forecasting day-ahead electricity prices

Abstract

Electricity price forecasting is essential for reliable and cost-effective operations in the power industry. However, the complex and nonlinear structure of the electricity price series presents uncertainties and challenges for energy management. To address this, artificial intelligence models such as SARIMAX, LSTM, and CNN-LSTM have been developed to predict short-term electricity prices. These models were tested using Mean Absolute Error, Root -Mean Squared Error, Mean Absolute Percentage Error, and percentage accuracy to verify their accuracy and to compare forecasting methodologies. The study includes the Diebold-Mariano test to confirm the statistical significance of the difference between the forecast errors of the two models. Ensemble learning was used to optimize a CNN-LSTM model, which automatically selects the best model by using CNN to extract valuable characteristics and LSTM to recognize data dependency in time series. Historical data from the German electrical market were used to validate the models' prediction performance. The results showed that the LSTM and CNN-LSTM models outperformed the SARIMAX model in terms of accuracy and simplicity, with the CNN-LSTM technique having significant forecasting advantages. These methods can be used for intelligent optimization forecasting of electricity prices.