EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
arXiv:2606.00506v1 Announce Type: cross Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have been employed for better prediction performance, existing works have two key limitations: (1) they usually formulate this task as a purely time-series prediction problem without explicitly modeling the spatial dependencies among different regions, and (2) they fail to provide reliable predictions with uncertainty estimates under abnormal situations such as extreme we
The increasing demand for efficient energy management given growing consumption and volatile supply necessitates more accurate and reliable prediction models that can handle complex spatial dependencies and uncertainties.
Improved energy consumption prediction is crucial for optimizing grid stability, enabling better resource allocation, and supporting the integration of renewable energy sources, all of which are critical for economic and environmental sustainability.
This research introduces an AI model that explicitly incorporates spatial dependencies and uncertainty quantification into energy prediction, offering a more robust approach than current time-series methods.
- · Energy Grid Operators
- · Smart City Planners
- · Renewable Energy Companies
- · AI/ML Infrastructure Providers
- · Inefficient Energy Consumers
- · Legacy Energy Prediction Software Vendors
More precise energy forecasting improves grid resilience and reduces waste, stabilizing energy costs.
Enhanced prediction capabilities facilitate greater adoption of distributed energy resources and microgrids, decentralizing energy infrastructure.
Reduced energy waste and more efficient resource allocation contribute to lower carbon emissions and accelerated progress towards climate goals.
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