Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the ov...Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the overall grid reliability,efficiency,and cost-effectiveness.This study introduces a novel D3Net model for half-hourly EP prediction,integrating Seasonal-Trend decomposition using LOESS(STL)and Variational ModeDecomposition(VMD)with Multi-Layer Perceptron(MLP),Random Forest Regression(RFR),and Tabular Neural Network(TabNet).The methodology involves applying STL tothe EP time-series to extract trend,seasonal,and residual components.The trend ispredicted using an MLP model,the seasonal component is further decomposed withVMD into 20 Variational Mode Functions(VMFs)and predicted using an RFR model,andthe residual component is decomposed with VMD and predicted using the TabNet model.Input features are identified using the Partial Autocorrelation Function,and models areoptimized using the Optuna algorithm.The final prediction combines the trend,seasonal,and residual components'predictions.Explainable Artificial Intelligence(xAI)methodswere used to enhance model interpretability and trustworthiness,with optimization viathe Optuna algorithm.Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statisticalsignificance of the D3Net model.The D3Net achieved the highest global performanceindicator for South Australia(GPI≈11.068)and Tasmania(GPI≈12.206).Theseresults validate the efficacy and statistical significance of the D3Net model,demonstrating the viability of integrating STL and VMD decomposition approaches withMLP,RFR,and TabNet for EP prediction.展开更多
文摘Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the overall grid reliability,efficiency,and cost-effectiveness.This study introduces a novel D3Net model for half-hourly EP prediction,integrating Seasonal-Trend decomposition using LOESS(STL)and Variational ModeDecomposition(VMD)with Multi-Layer Perceptron(MLP),Random Forest Regression(RFR),and Tabular Neural Network(TabNet).The methodology involves applying STL tothe EP time-series to extract trend,seasonal,and residual components.The trend ispredicted using an MLP model,the seasonal component is further decomposed withVMD into 20 Variational Mode Functions(VMFs)and predicted using an RFR model,andthe residual component is decomposed with VMD and predicted using the TabNet model.Input features are identified using the Partial Autocorrelation Function,and models areoptimized using the Optuna algorithm.The final prediction combines the trend,seasonal,and residual components'predictions.Explainable Artificial Intelligence(xAI)methodswere used to enhance model interpretability and trustworthiness,with optimization viathe Optuna algorithm.Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statisticalsignificance of the D3Net model.The D3Net achieved the highest global performanceindicator for South Australia(GPI≈11.068)and Tasmania(GPI≈12.206).Theseresults validate the efficacy and statistical significance of the D3Net model,demonstrating the viability of integrating STL and VMD decomposition approaches withMLP,RFR,and TabNet for EP prediction.
