# Jinbao Wang and coauthors propose Adaptive Residual Modeling for Scenario Learning [Canonical reading](https://escapemeridian.com/readings/jinbao-wang-and-coauthors-propose-adaptive-residual-modeling-2f6fbf23) Jinbao Wang and coauthors propose adaptive residual modeling for scenario learning (ARMSL), a two-stage approach to probabilistic load forecasting. Their paper addresses a trade-off: fitting the full forecast distribution can underemphasize rare extremes, while heavy tail-weighting can distort that distribution. Stage I models environmental response, load patterns, persistent residual deviations and remaining innovations, then generates joint active- and reactive-power scenarios. Stage II leaves those scenarios fixed and calibrates extreme marginal quantiles using a reference forecast, admitting only outward deviations according to local calibration history and operating state. In numerical simulations on FeederBW, the authors report that Stage I outperforms diffusion baselines on energy and variogram scores. Their complete framework reports a 0.1932 active-power 0.99-quantile pinball loss, with a 1.054% exceedance rate against a nominal 1%. On Rolle, they report losses 5.39%–14.40% lower than the active-power-only anchor. They also report that Stage I scenarios have a 0.953 correlation between predicted and observed feeder event rates, with 90.0% of feeder errors within two percentage points. These are simulation results on the named datasets; the abstract does not establish how the method performs beyond them. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv eess.SY · Article Original source date: Nov 2, 2026 (2026-11-02). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T06:53:12.169Z. First observed: 2026-11-03T08:51:44.875Z. Retrieved: 2026-11-03T21:16:48.871Z. - [Adaptive Residual Modeling for Scenario Learning in Probabilistic Load Forecasting](https://arxiv.org/abs/2611.03049) ## Limits The evidence is the paper's abstract, which reports numerical simulations on FeederBW and Rolle; it does not establish performance beyond those datasets. The abstract does not provide details of the simulation setup or the reported metrics' uncertainty. ## Open question How would ARMSL's reported results compare with its performance on datasets beyond FeederBW and Rolle? ## Source-stated The authors propose ARMSL as a two-stage framework for probabilistic load forecasting. > this paper proposes adaptive residual modeling for scenario learning (ARMSL), a two-stage framework. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated The authors say standard distribution-fitting objectives may underemphasize rare extremes, while heavy tail-weighting can distort the probability distribution. > standard objectives that fit the full forecast distribution often underemphasize rare observations and extremes. Conversely, applying heavy tail-weighting in the distribution to capture these extremes can distort the underlying probability distribution. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using an anchor; only outward deviations are admitted. > Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using a simple, channel-specific reference forecast (an anchor). Only outward deviations from this anchor are admitted - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated In FeederBW simulations, the authors report a 0.1932 active-power 0.99-quantile pinball loss and a 1.054% exceedance rate against a nominal 1%. > active-power 0.99-quantile pinball loss of 0.1932 (a 30.08% to 85.33% reduction relative to three baselines) with 1.054% exceedance rate against a nominal 1%. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated In Rolle simulations, the authors report losses 5.39%–14.40% lower than the active-power-only anchor. > From simulations on the Rolle dataset, we observe decreased losses by 5.39%--14.40% compared to the active-power-only anchor. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated The authors report a 0.953 correlation between predicted and observed feeder event rates, with 90.0% of feeder errors within two percentage points. > the unchanged Stage I scenarios in the simulation yield a correlation of 0.953 between predicted and observed feeder event rates, with 90.0% of feeder errors falling within two percentage points. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source and contributor context Wang, Jinbao, Liu, Jun, Zhang, Shiliang, Ma, Xuehui, Li, Yushuai, Yan, Lin · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 13dcab1d-9afc-43b7-8b1c-11d96f848ea9. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T06:53:12.169Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: c794b1f47e08d6471a3d004cdcd763a39151ae454ca1081ba8b5a0e3c523562c. Source text hash: 3638a6e810bc407b95b05ae2acfbfd55763f8807d4cc57d01eb5e5223de92252. ## Revision and source-check history ### Version 1 — 2026-11-10T06:53:12.169Z Jinbao Wang and coauthors propose adaptive residual modeling for scenario learning (ARMSL), a two-stage approach to probabilistic load forecasting. Their paper addresses a trade-off: fitting the full forecast distribution can underemphasize rare extremes, while heavy tail-weighting can distort that distribution. Stage I models environmental response, load patterns, persistent residual deviations and remaining innovations, then generates joint active- and reactive-power scenarios. Stage II leaves those scenarios fixed and calibrates extreme marginal quantiles using a reference forecast, admitting only outward deviations according to local calibration history and operating state. In numerical simulations on FeederBW, the authors report that Stage I outperforms diffusion baselines on energy and variogram scores. Their complete framework reports a 0.1932 active-power 0.99-quantile pinball loss, with a 1.054% exceedance rate against a nominal 1%. On Rolle, they report losses 5.39%–14.40% lower than the active-power-only anchor. They also report that Stage I scenarios have a 0.953 correlation between predicted and observed feeder event rates, with 90.0% of feeder errors within two percentage points. These are simulation results on the named datasets; the abstract does not establish how the method performs beyond them. ## Original source arXiv eess.SY · Article Original source date: Nov 2, 2026 (2026-11-02). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T06:53:12.169Z. First observed: 2026-11-03T08:51:44.875Z. Retrieved: 2026-11-03T21:16:48.871Z. - [Adaptive Residual Modeling for Scenario Learning in Probabilistic Load Forecasting](https://arxiv.org/abs/2611.03049) ## Limits The evidence is the paper's abstract, which reports numerical simulations on FeederBW and Rolle; it does not establish performance beyond those datasets. The abstract does not provide details of the simulation setup or the reported metrics' uncertainty. ## Open question How would ARMSL's reported results compare with its performance on datasets beyond FeederBW and Rolle? ## Source-stated The authors propose ARMSL as a two-stage framework for probabilistic load forecasting. > this paper proposes adaptive residual modeling for scenario learning (ARMSL), a two-stage framework. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated The authors say standard distribution-fitting objectives may underemphasize rare extremes, while heavy tail-weighting can distort the probability distribution. > standard objectives that fit the full forecast distribution often underemphasize rare observations and extremes. Conversely, applying heavy tail-weighting in the distribution to capture these extremes can distort the underlying probability distribution. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using an anchor; only outward deviations are admitted. > Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using a simple, channel-specific reference forecast (an anchor). Only outward deviations from this anchor are admitted - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated In FeederBW simulations, the authors report a 0.1932 active-power 0.99-quantile pinball loss and a 1.054% exceedance rate against a nominal 1%. > active-power 0.99-quantile pinball loss of 0.1932 (a 30.08% to 85.33% reduction relative to three baselines) with 1.054% exceedance rate against a nominal 1%. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated In Rolle simulations, the authors report losses 5.39%–14.40% lower than the active-power-only anchor. > From simulations on the Rolle dataset, we observe decreased losses by 5.39%--14.40% compared to the active-power-only anchor. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source-stated The authors report a 0.953 correlation between predicted and observed feeder event rates, with 90.0% of feeder errors within two percentage points. > the unchanged Stage I scenarios in the simulation yield a correlation of 0.953 between predicted and observed feeder event rates, with 90.0% of feeder errors falling within two percentage points. - [Supporting source](https://arxiv.org/abs/2611.03049) ## Source and contributor context Wang, Jinbao, Liu, Jun, Zhang, Shiliang, Ma, Xuehui, Li, Yushuai, Yan, Lin · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 13dcab1d-9afc-43b7-8b1c-11d96f848ea9. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T06:53:12.169Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: c794b1f47e08d6471a3d004cdcd763a39151ae454ca1081ba8b5a0e3c523562c. Source text hash: 3638a6e810bc407b95b05ae2acfbfd55763f8807d4cc57d01eb5e5223de92252. ### uncertain — 2026-11-10T06:53:12.169Z — anthropic/claude-opus-5.5 Publication: 13dcab1d-9afc-43b7-8b1c-11d96f848ea9 attribution — pass: The reading correctly credits the proposal and results to Jinbao Wang and coauthors. It frames the results as author-reported, using phrases such as 'the authors report', and does not present them as established fact. > Authors:Jinbao Wang, Jun Liu, Shiliang Zhang, Xuehui Ma, Yushuai Li, Lin Yan summary — uncertain: The reading tracks the abstract faithfully, but a reader without specialist background may not see that it concerns electricity grids. It never says plainly that load forecasting predicts power demand on a feeder, a local distribution line. It drops the source's stake: grid operations need forecasts with reliable extreme ranges. Central terms (quantile, pinball loss, exceedance rate, variogram score) have no plain gloss or tooltip. It also gives the 0.1932 loss without the source's 30.08%–85.33% reduction against three baselines, so the number has no meaning on its own. Smallest repair: add the operations-need sentence and the baseline comparison, and gloss these terms. > Effective power system operations require both multi-horizon load forecasts and well-calibrated extreme quantiles for the forecast. date — pass: The originalPublished date of 2026-11-02 matches the arXiv v1 submission date. The later feed date is not used. > \[Submitted on 2 Nov 2026\] support — pass: Each claim matches the abstract verbatim, including the figures for FeederBW, Rolle and the correlation result. The limitations and the question about other datasets follow from the simulation-only evidence and do not overreach. No benefits or costs are invented. > From simulations on the Rolle dataset, we observe decreased losses by 5.39%--14.40% compared to the active-power-only anchor. media — pass: The selected media is the verified arXiv abstract page for this same paper. It is the direct original source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/2f6fbf23c6fc3d2f24147277f15cecadae5f0ccd878a9adc39e05d707b7159ff/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/jinbao-wang-and-coauthors-propose-adaptive-residual-modeling-2f6fbf23.json)