PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting
arXiv:2605.21550v1 Announce Type: new Abstract: Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk management. However, existing methods face three limitations. First, they adopt a two-stage predict-then-locate paradigm, which severs the link between temporal localization and intensity regression. Second, they still struggle with the multi-scale representation conflict, leading to peak misjudgment and timing misalignment. Third, the lack of explicit peak timing context during intensity regressi
The increasing complexity and demand on electricity grids driven by electrification and distributed energy resources necessitate more sophisticated forecasting tools to prevent failures and optimize operations.
Accurate electricity load forecasting is critical for grid stability, cost efficiency, and the integration of renewable energy, directly impacting economic productivity and energy security.
This new multi-scale framework promises to improve the precision of both peak timing and intensity prediction, moving beyond existing two-stage methods that sever this crucial link.
- · Grid operators
- · Energy trading firms
- · Renewable energy companies
- · AI/ML solution providers for utilities
- · Legacy forecasting software providers
- · Inefficient grid management practices
Improved grid stability and reduced operational costs through more accurate load forecasting.
Better integration of intermittent renewable energy sources, accelerating the energy transition.
Reduced risk of blackouts and brownouts, leading to higher industrial output and societal resilience.
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Read at arXiv cs.LG