Hybrid CNN-GRU model for monthly electricity price forecasting: Performance evaluation on limited multivariable time-series Data - Yaprakdal, Fatma (Edelweiss Applied Science and Technology, 2024)
Understanding electricity pricing dynamics is crucial for market participants, as prices reflect supply and demand balances. Accurate medium-term price prediction aids in maintenance scheduling, expansion planning, and contracting, but poses challenges due to a long forecasting horizon and limited explanatory data. This paper proposes a hybrid forecasting system that merges Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for estimating monthly electricity prices. The CNN performs feature extraction, while the GRU handles temporal regression. We evaluate model performance using mean absolute percentage error (MAPE) and the coefficient of determination (R²). Experimental results indicate that our model outperforms both popular deep learning (DL) methods (GRU, LSTM) and machine learning (ML) techniques (SVR, RF, XGBoost), confirming the feasibility and effectiveness of this approach for accurate electricity price prediction.