PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting

2021-01-01 · Source: cs.LG updates on arXiv.org · Field: Energy & Utilities — Artificial Intelligence & Machine Learning, Data Science & Analytics, Energy Storage & Grid Technology · Depth: Expert, extended

Summary

PeakFocus is a unified framework designed for Electricity Load Peak Forecasting (ELPF), which simultaneously predicts peak timing and intensity. It addresses three key limitations of existing methods: the separation of temporal localization and intensity regression, multi-scale representation conflicts causing misjudgment and misalignment, and intensity smoothing due to absent peak timing context. The framework integrates a Unified Peak-Aware Pipeline (UPAP) with a triple hybrid loss for joint supervision, a Multi-Scale Mixing Peak Locator (MSM-PL) to resolve multi-scale conflicts, and a Location-Aware Decoder (LAD) that injects peak timing context. Evaluated on the public ELC dataset and the industrial-scale World Large-scale Electricity Load (WLEL) dataset (41,476 records, 2,944 structural peaks, 7.10% of timestamps), PeakFocus consistently outperforms baselines, achieving approximately 7% higher ℋ₁ and 15% lower TP-MSE on WLEL, demonstrating improved timing precision and intensity estimation.

Key takeaway

For Machine Learning Engineers developing electricity load forecasting solutions, if your current models struggle with accurately predicting peak timing and intensity, you should consider adopting a unified framework like PeakFocus. Its joint localization and regression, multi-scale feature mixing, and context-aware decoding directly address common issues like intensity smoothing and timing misalignment, offering significantly improved precision for critical grid management decisions.

Key insights

PeakFocus unifies peak timing localization and intensity regression to overcome smoothing and misalignment in electricity load forecasting.

Principles

• Jointly supervise peak timing and intensity regression.
• Employ multi-scale feature mixing for precise peak localization.
• Condition intensity regression on explicit peak timing context.

Method

PeakFocus employs a dual-head architecture: MSM-PL resolves localization conflicts via multi-scale mixing, and LAD prevents intensity smoothing by conditioning regression on peak timing context via Context Gate Fusion. A triple hybrid loss optimizes the pipeline.

In practice

• Implement tolerance-based evaluation for sparse event forecasting.
• Utilize soft Gaussian masks for robust peak supervision.

Topics

• Electricity Load Forecasting
• Peak Prediction
• Multi-Scale Time Series
• Attention Mechanisms
• Hybrid Loss Functions
• Tolerance-based Evaluation

Code references

• TongjiFinLab/PeakFocus
• erdogant/findpeaks
• TongjiFinLab/PeakFocus0
• TongjiFinLab/PeakFocus1

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.