arXiv:2605.29072v1 Announce Type: new Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, however, complete and timely measurements may not always be available, and the observed data can be partial, noisy, or delayed. This motivates the use of learned forecasting models for predicting the evolving consumption state, together with data assimilation methods for sequential forecast correction. In this work, we study a high-dimensional data assimilation problem for real energy-consumption d
The increasing reliance on AI for predictive modeling across critical infrastructure, combined with the growing challenges of data quality and availability, makes robust correction mechanisms for forecasts particularly timely.
Accurate energy forecasting is crucial for grid stability, efficient resource allocation, and managing the economic impact of energy price volatility, directly affecting industries and consumers.
This development proposes a method to improve the reliability of energy consumption forecasts, enabling better operational decisions in power systems, especially with imperfect real-world data.
- · Energy utilities
- · Smart grid developers
- · AI/ML model providers
- · Energy traders
- · Inefficient power generators
- · Manual forecasting systems
Improved energy forecast accuracy leads to more efficient power grid management and reduced operational costs.
Enhanced grid stability and resource optimization could enable greater integration of intermittent renewable energy sources.
More reliable energy markets attract further investment in critical infrastructure and advanced energy technologies, potentially mitigating future energy crises.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG