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<p>To adapt to the complex and volatile environment of the electricity spot market, this study proposes a flexible resource characterization and identification method for Integrated Energy Systems (IES). To address the non-stationarity of multi-energy loads, a Variational Mode Decomposition (VMD) enhanced Temporal Convolutional Network-Graph Convolutional Network-Long Short-Term Memory (TCN-GCN-LSTM) spatiotemporal fusion model is developed, achieving significant improvements in forecasting accuracy compared to benchmark models. For electricity price forecasting, a hybrid Random Forest-Improved Attribute Generalization Importance Value-Complete Ensemble Empirical Mode Decomposition with Sample Entropy-Long Short-Term Memory (RF-IAGIV-CEEMD-SE-LSTM) model is constructed, which combines feature selection, subsequence decomposition, and noise reduction to capture temporal dynamics. Experimental results demonstrate that the proposed models reduce RMSE by up to 42.7% across load types and keep market-clearing deviations within 3% under multiple scenarios. The contributions of this study lie in three aspects: (1) developing a collaborative framework for multi-energy load and price forecasting; (2) proposing advanced spatiotemporal feature extraction and hybrid data preprocessing strategies; and (3) providing case-based validation with diverse market architectures. These results highlight the method’s strong potential for supporting intelligent scheduling and decision-making in modern electricity spot markets.</p>

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An integrated energy system flexible resource feature extraction and identification method for electricity spot market

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Fang Tang,
  • Zhenlan Dou,
  • Yuchen Cao,
  • Chunyan Zhang

摘要

To adapt to the complex and volatile environment of the electricity spot market, this study proposes a flexible resource characterization and identification method for Integrated Energy Systems (IES). To address the non-stationarity of multi-energy loads, a Variational Mode Decomposition (VMD) enhanced Temporal Convolutional Network-Graph Convolutional Network-Long Short-Term Memory (TCN-GCN-LSTM) spatiotemporal fusion model is developed, achieving significant improvements in forecasting accuracy compared to benchmark models. For electricity price forecasting, a hybrid Random Forest-Improved Attribute Generalization Importance Value-Complete Ensemble Empirical Mode Decomposition with Sample Entropy-Long Short-Term Memory (RF-IAGIV-CEEMD-SE-LSTM) model is constructed, which combines feature selection, subsequence decomposition, and noise reduction to capture temporal dynamics. Experimental results demonstrate that the proposed models reduce RMSE by up to 42.7% across load types and keep market-clearing deviations within 3% under multiple scenarios. The contributions of this study lie in three aspects: (1) developing a collaborative framework for multi-energy load and price forecasting; (2) proposing advanced spatiotemporal feature extraction and hybrid data preprocessing strategies; and (3) providing case-based validation with diverse market architectures. These results highlight the method’s strong potential for supporting intelligent scheduling and decision-making in modern electricity spot markets.