VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting
arXiv:2509.15394v3 Announce Type: replace Abstract: Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal patterns crucial. Decomposition techniques make such structure explicit and thereby improve predictive performance. Variational Mode Decomposition (VMD) is a powerful signal-processing method for periodicity-aware decomposition and has seen growing adoption in recent years. However, existing studies often suffer from information leakage and rely on inappropriate hyperparam
The increasing complexity and volatility of electricity grids, driven by renewable energy integration and rising demand, necessitate more sophisticated and accurate forecasting methods.
Improved electricity demand forecasting is critical for grid stability, resource allocation, and optimizing energy infrastructure, directly impacting operational efficiency and sustainability.
This advancement in VMD potentially enhances the reliability and efficiency of energy management systems by providing more accurate predictions, crucial for balancing supply and demand.
- · Energy grid operators
- · Renewable energy companies
- · Smart city developers
- · AI/ML energy solution providers
- · Traditional forecasting models
- · Infrequent energy traders
More efficient energy distribution and reduced instances of power outages due to better predictive capabilities.
Reduced operational costs for energy providers and potentially more stable electricity prices for consumers.
Accelerated adoption of AI-driven grid management systems, potentially enabling more dynamic and responsive energy markets.
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Read at arXiv cs.LG