Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach

Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach

Accurate prediction of electricity price (𝐸𝑃 ) is crucial for energy utilities and grid operators for enhancing the energy trading, grid stability studies, resource allocations and pricing strategies, thereby improving the overall grid reliability, efficiency, and cost-effectiveness. This study introduces a novel D3Net model for halfhourly 𝐸𝑃 prediction, integrating Seasonal-Trend decomposition using LOESS (STL) and Variational Mode Decomposition (VMD) with Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and Tabular Neural Network (TabNet). The methodology involves applying STL to the 𝐸𝑃 time-series to extract trend, seasonal, and residual components. The trend is predicted using an MLP model, the seasonal component is further decomposed with VMD into 20 Variational Mode Functions (VMFs) and predicted using an RFR model, and the residual component is decomposed with VMD and predicted using the TabNet model. Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm The final prediction combines the trend, seasonal, and residual components’ predictions. Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statistical significance of the D3Net model. The D3Net achieved the highest global performance indicator for South Australia (𝐺𝑃 𝐼 ≈ 11.068) and Tasmania (𝐺𝑃 𝐼 ≈ 12.206). These results validate the efficacy and statistical significance of the D3Net model, demonstrating the viab