TL;DR
A novel Context-Enhanced NBEATSx (CE-NBEATSx) model based on a Temporal Convolutional Network (TCN) Context Encoder and a Multi-Head Cross-Attention (MHCA) mechanism that outperforms the evaluated forecasting models.
Electricity price forecasting is critical for electricity markets with high renewable energy penetration, where increasing wind generation introduces significant volatility and uncertainty into price formation. Although deep learning and signal decomposition methods have shown promising results, studies jointly address...
Overall, the study demonstrates that recurrent deep learning architectures provide reliable and operationally relevant solutions for electricity consumption forecasting in highly structured energy demand series.
With the accelerating transition toward low-carbon power systems, dynamic carbon emission factor (DCEF) forecasting has become increasingly important for carbon accounting and real-time carbon management. However, the complex nonlinearity, strong temporal dependency, and occasional near-zero fluctuations in DCEF sequen...
The findings demonstrate that the integration of SWT-based multiscale representation, selective frequency weighting, and TCN-based temporal dependency modeling provides an effective and robust framework for short-term campus electricity consumption forecasting.
The experimental findings indicate that the proposed methodology yields consistent falls in error in comparison with the general statistical and deep learning baselines on the datasets analyzed, and the results show that the methodology has been improved in terms of R2 in forecasts.
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