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 |---|---|---|---|
 | Field guide 实务指南 | [field_guide_en.md](us_market/field_guide_en.md) | EN only | Bidding, trading, and the impact of AI — consolidated working discussions, July 2026. 竞价、交易与 AI 影响综合笔记。 |
 | Economists survey 经济学家综述 | [economists_survey_en.md](us_market/economists_survey_en.md) | [economists_survey_zh.md](us_market/economists_survey_zh.md) | How economists shaped US electricity markets — theory, practice, milestones. 经济学家如何塑造美国电力市场——理论、实践与里程碑。 |
+| Forecasting open ecosystem 预测开放生态 | [forecasting_open_ecosystem_en.md](us_market/forecasting_open_ecosystem_en.md) | EN only | Open US wholesale-market datasets, models, benchmarks, structural tools, and evaluation design for price, load, renewable, and capacity forecasting. 美国批发电力市场预测的开放数据、模型、基准、结构化工具与评估设计。 |
 
 ### `pjm/` — US market 美国市场
 
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+# Open Models, Datasets, and Benchmarks for Forecasting in US Wholesale Power Markets
+
+**Scope:** Day-ahead (DA) and real-time (RT) prices, load/demand, renewable generation, and capacity in the seven US ISO/RTO markets (CAISO, ERCOT, PJM, MISO, SPP, NYISO, ISO-NE).
+**Status:** Compiled and verified July 2026. Everything listed is free/open unless flagged otherwise. Web-verified items are marked ✅; items from general domain knowledge that you should re-confirm before relying on are marked ◻︎.
+
+---
+
+## 1. How to read this report
+
+Forecasting in US wholesale markets breaks into four related but distinct problems, each with its own data and benchmark ecosystem:
+
+| Target | Typical granularity | Open-benchmark maturity |
+|---|---|---|
+| DA prices (LMPs) | Hourly, nodal/zonal/hub | Moderate (one canonical academic benchmark, aging) |
+| RT prices / DA–RT spread | 5-min–hourly, nodal | Weak (no standard benchmark) |
+| Load | 5-min–hourly, BA/zone | Strong (GEFCom + government data) |
+| Wind/solar (price drivers) | 5-min–hourly, site/zone | Strong (NREL/ARPA-E datasets) |
+| Capacity (installed & pipeline) | Monthly/annual, unit/project | Strong data, no "forecasting benchmark" per se |
+
+A key structural fact: **there is no single canonical benchmark for US price forecasting the way ImageNet exists for vision.** The academic standard (Section 4) uses one aging PJM zone; practitioners assemble bespoke datasets from ISO portals using the tools in Section 2. Treat Section 2 as the foundation and Sections 4–10 as what's built on top of it.
+
+---
+
+## 2. The data-access layer (start here)
+
+### 2.1 gridstatus ✅
+
+**Repo:** https://github.com/gridstatus/gridstatus • **Docs:** https://opensource.gridstatus.io
+
+The de facto standard open-source Python library for US grid data. Covers CAISO, ERCOT, PJM, MISO, SPP, NYISO, ISO-NE (plus Canada's IESO and AESO, and EIA), with a standardized API across sources. Available datasets (varying by ISO): load, load forecasts, fuel mix, DA and RT LMPs, ancillary service prices, storage, solar/wind forecasts, generation outages, and interconnection queues.
+
+Why it matters for forecasting specifically:
+
+- It exposes the ISOs' **own published forecasts** (load, wind, solar) — these are both model features and the operational baselines any new model must beat.
+- It normalizes ISO quirks (e.g., it flips NYISO's congestion sign convention to match other ISOs; handles ERCOT's API migration).
+- Caveats: history depth varies by ISO and dataset; some ISO endpoints require free registration/API keys (PJM Data Miner, ERCOT public API, ISO-NE web services); nodal LMP pulls are large (PJM has >10,000 pricing nodes — a single day of hourly RT nodal data is ~300k rows).
+
+The same team runs a hosted API (gridstatus.io) with a free tier and a data catalog — convenient, but the open-source library against ISO portals is fully free.
+
+### 2.2 Raw ISO portals (free, authoritative) ◻︎
+
+All seven ISOs publish DA/RT prices, load, and forecasts publicly. These are the ground-truth sources gridstatus wraps:
+
+| ISO | Portal | Notes |
+|---|---|---|
+| CAISO | OASIS (oasis.caiso.com) | DA hourly + RT 15-min/5-min LMPs at all nodes; no registration needed ✅ |
+| ERCOT | MIS / ERCOT Public API (data.ercot.com) | SPPs by settlement point; 60-day SCED/DAM disclosure files are uniquely rich (bid curves, telemetry, released with 60-day lag) |
+| PJM | Data Miner 2 (dataminer2.pjm.com) | Free API key; deep LMP history, load, forecasts, outages |
+| MISO | Data Exchange / Market Reports (miso.com) | API-based access rolled out mid-2020s; check current endpoints |
+| SPP | Marketplace portal / SPP file server | LMPs, MTLF (load forecast), wind forecasts |
+| NYISO | OASIS + open CSV archives (mis.nyiso.com) | Among the easiest bulk downloads; zonal + nodal, decades of history |
+| ISO-NE | Web Services API / ISO Express | Free registration; hourly DA/RT LMPs, load, forecasts |
+
+Two ISO-specific gems worth knowing: ERCOT's **60-day disclosure** data (actual resource-level offers and dispatch, the only place in the US you can see near-complete bid stacks, lagged 60 days) and PJM/MISO **generation outage** reports, which are first-order features for RT price models.
+
+### 2.3 EIA (federal) ✅
+
+- **EIA API v2** (api.eia.gov, free key): hourly demand, DA demand forecast, net generation by fuel, and interchange for every US balancing authority via **Form EIA-930** (the "Hourly Electric Grid Monitor"), from July 2015 onward. Also fuel prices, monthly generation, and much more.
+- **Cleaned EIA-930 demand** (Ruggles, Farnham et al.): screened + MICE-imputed hourly demand for the 56 demand-reporting BAs in CONUS. Methods paper: *Scientific Data* 7, 155 (2020), doi:10.1038/s41597-020-0483-x. Data versioned on Zenodo (doi:10.5281/zenodo.3517196) and GitHub (`truggles/EIA_Cleaned_Hourly_Electricity_Demand_Data`). The January 2025 release covers 2020-01-01 → 2025-01-01 including BA subregions. This is the standard cleaned load dataset in academic work — raw EIA-930 contains missing/anomalous values you must otherwise handle yourself.
+- Note EIA-930's known-issues guide: BA-reported data has quirks (timezone conventions, revisions); PUDL (below) retains only UTC timestamps for this reason.
+
+### 2.4 PUDL (Catalyst Cooperative) ✅
+
+**Docs:** https://catalystcoop-pudl.readthedocs.io • **Repo:** catalyst-cooperative/pudl
+
+Open data pipeline that cleans and links **EIA-860/860M/861/923, EIA-930, FERC Form 1, and EPA CEMS** into analysis-ready tables (Parquet/SQLite, also on Datasette and Kaggle). This is the go-to source for:
+
+- **Capacity:** unit-level generator characteristics, capacities, fuel, planned additions and retirements (EIA-860/860M).
+- **Generation & costs:** monthly unit generation and fuel costs (EIA-923), utility financials (FERC 1).
+- **Hourly unit-level operation:** EPA CEMS hourly gross load and emissions for fossil units — effectively an open proxy for historical unit commitment, invaluable for structural price models.
+- EIA-930 demand/forecast/interchange tables (all but hourly CO₂ available through PUDL).
+
+### 2.5 Weather (the other half of every model) ◻︎
+
+- **NOAA HRRR** (3-km, hourly, CONUS; free on AWS/Google/Azure via NODD) — the workhorse for operational US load/renewables features; `herbie` (Python) is the standard access library.
+- **GFS/ECMWF open data** for longer horizons; **ERA5** (Copernicus) for reanalysis/backtesting.
+- **NOAA ISD/ASOS** station observations for actuals.
+- **Open-Meteo** — free API conveniently exposing HRRR/GFS/ECMWF forecasts and historical archives.
+- Caution for backtesting: use **archived forecasts** (as-of data), not reanalysis, for features that would have been available at prediction time. HRRR archives on AWS go back to ~2014.
+
+---
+
+## 3. What "the benchmark to beat" means operationally
+
+Before any ML: every ISO publishes its **own DA load forecast**, and several publish wind/solar forecasts (all retrievable via gridstatus). For prices, the DA LMP itself is the market's consensus forecast of RT conditions, and simple benchmarks (persistence, naive-with-seasonality, DA price as RT predictor) are strong. Any serious study should report skill relative to these, not just absolute error.
+
+---
+
+## 4. Price forecasting (DA and RT)
+
+### 4.1 The canonical academic benchmark: Lago et al. / epftoolbox ✅
+
+**Paper:** Lago, Marcjasz, De Schutter, Weron, "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," *Applied Energy* 293:116983 (2021).
+**Code/data:** https://github.com/jeslago/epftoolbox (AGPL-3.0) • docs: epftoolbox.readthedocs.io
+
+What it provides:
+
+- **Five open datasets, six years each** of hourly DA prices + two exogenous forecast series per market: EPEX-BE, EPEX-FR, EPEX-DE, Nord Pool, and **PJM (ComEd zone)** — the one US market in the set (data ends 2018).
+- **Two open reference models**: LEAR (LASSO-estimated autoregressive) and a hyperparameter-optimized DNN, implementable on any DA market without expert tuning.
+- **Evaluation infrastructure**: MAE/sMAPE/MASE-style metrics plus **Diebold-Mariano and Giacomini-White** statistical tests, and pre-computed state-of-the-art forecasts so you can compare without retraining anything.
+- A widely cited **best-practices checklist** (long rolling test windows, open data, statistical testing, strong simple baselines) — arguably the paper's most durable contribution.
+
+Limitations for US use: only one US zone, data ends in 2018 (pre-dating today's storage/renewables-driven price regimes and events like Winter Storm Uri), zonal not nodal, and its exogenous features are minimal. It remains the field's shared yardstick; hundreds of follow-up papers (single- vs multi-output NNs, distributional networks, XAI studies, transformer comparisons) evaluate on it.
+
+### 4.2 Open probabilistic-EPF methods (built on the same benchmark) ◻︎
+
+The Weron group (Wrocław) and collaborators maintain open implementations that are the standard probabilistic baselines: **Quantile Regression Averaging (QRA)** and variants, **distributional deep networks** (e.g., DDNN with Normal/JSU output distributions — Marcjasz et al. 2023), **Isotonic Distributional Regression**, **conformal-prediction** approaches, and the **Schaake shuffle** for multivariate (24-hour joint) probabilistic forecasts. Most have code on GitHub keyed to the epftoolbox datasets. Metrics: pinball loss, CRPS, coverage; tests: DM/GW.
+
+### 4.3 US-specific and RT-price work: no standard benchmark ✅ (the gap is verified)
+
+- **RT prices and the DA–RT ("DART") spread** are studied mostly in the virtual-bidding/trading literature (e.g., LSTM/seq2seq nodal DART forecasting, spike-classification papers such as "Forecasting electricity DART spikes"). Papers overwhelmingly use self-assembled ISO data; code is sometimes released, standardized datasets essentially never.
+- Recent US DA studies (e.g., ISO-NE forecasting with public ISO data + published demand forecasts and generation-capacity data, arXiv:2405.14893) illustrate the pattern: public data, bespoke splits.
+- Scattered open repos exist (e.g., CAISO LMP ML forecasts with paired load-weather datasets on GitHub) but are individual projects, not maintained benchmarks.
+
+**Practical implication:** for US nodal/RT work you will build your own benchmark from Section 2 sources. Standard recipe: DA + RT LMPs (target), ISO load/wind/solar forecasts, generation and transmission outages, net-load features, gas prices (Section 11 caveat), calendar features; rolling-origin evaluation over ≥1 year including at least one summer and one winter.
+
+### 4.4 Open models for price forecasting ◻︎ (tiered)
+
+Only the first tier below is actually price-specific; the rest are general tools applied to prices.
+
+**Tier 1 — Purpose-built open EPF reference models.** The epftoolbox suite (Section 4.1) is the only widely accepted open model set built specifically for DA price forecasting: **LEAR** (LASSO-estimated autoregressive) and a hyperparameter-optimized **DNN**, both designed to run on any DA market without expert tuning. These are the baselines every new EPF paper is expected to beat, and the toolbox ships pre-computed forecasts so comparisons need no retraining. Practical note: LEAR is famously hard to beat cost-adjusted — a well-tuned LASSO on hourly price lags plus load/renewables forecasts captures most of the achievable skill.
+
+**Tier 2 — Open probabilistic EPF methods** (research code, largely from the Weron/Wrocław group): **Quantile Regression Averaging (QRA)** and variants, **distributional deep networks** (Normal / Johnson's SU outputs — Marcjasz et al. 2023), **isotonic distributional regression**, **conformal prediction** wrappers, and the **Schaake shuffle** for jointly coherent 24-hour probabilistic paths. Published with code, typically evaluated on epftoolbox data with pinball loss/CRPS; research artifacts rather than maintained packages, so expect assembly.
+
+**Tier 3 — General open frameworks applied to prices** (what most practitioners actually use):
+
+- **Gradient-boosted trees** (XGBoost/LightGBM) — the workhorse for tabular price features (load/renewable forecast lags, outages, gas proxies, calendar); competitive with or better than deep nets on spiky US nodal data in many settings.
+- **Nixtla neuralforecast** (N-BEATS, N-HiTS, TFT, PatchTST, TimesNet, DeepAR-style) and **statsforecast** (ARIMA/ETS/Theta baselines); **GluonTS** and **Darts** as alternative frameworks.
+
+**Tier 4 — Open-weight time-series foundation models:** Amazon **Chronos / Chronos-Bolt / Chronos-2**, Google **TimesFM (2.x)**, Salesforce **Moirai / Moirai-2**, **TiRex**, Datadog **Toto**, **Lag-Llama**. The honest read of the 2025–2026 literature: respectable zero-shot on load, but prices — spikes, negative values, regime shifts — remain a weak spot, and vanilla zero-shot use generally does not beat a tuned LEAR/GBM with proper exogenous features. Leakage is a live concern on public benchmarks (Section 8). They get interesting with fine-tuning or covariate-aware use (Chronos-2 accepts covariates natively).
+
+**Tier 5 — Structural / simulation-based models.** Open production-cost and OPF stacks — **PyPSA / PyPSA-USA**, NREL **Sienna** (PowerSimulations.jl), **Egret**, **UnitCommitment.jl** — run against open synthetic grids (RTS-GMLC, Texas 7k, Breakthrough Energy US test system) simulate LMPs from fuel prices, capacities, and topology. This is how you forecast *structural* price changes (entry, retirements, transmission) that purely statistical models cannot see.
+
+**What does not exist in the open:** a maintained, pretrained, off-the-shelf model for US nodal LMPs. Commercial vendors sell that; the open ecosystem composes it — typically GBM or a distributional NN per node/hub, epftoolbox-style evaluation, ISO forecasts as features.
+
+---
+
+## 5. Load forecasting
+
+### 5.1 GEFCom (the classic benchmarks) ◻︎
+
+The **Global Energy Forecasting Competitions** (Tao Hong et al.) remain the standard academic load benchmarks:
+
+- **GEFCom2012** — hierarchical zonal load (US utility) + temperature; point forecasting.
+- **GEFCom2014** — established **probabilistic** load (and price/wind/solar) forecasting with pinball loss; the reference dataset for quantile load models.
+- **GEFCom2017** — hierarchical probabilistic load on ISO-NE zones with rich weather.
+
+Data circulates via the competition papers (*IJF* 2014/2016/2019) and Dr. Hong's site (drhongtao.com). Age is the main limitation; they predate electrification/data-center load growth.
+
+### 5.2 System-level US load ✅
+
+- **EIA-930** (raw via EIA API, cleaned via Ruggles/Zenodo, tabulated via PUDL) — hourly demand + the BA's own DA forecast for every US BA since mid-2015. Forecasting the demand of specific BAs with the BA's published forecast as the baseline is a well-posed, fully open task.
+- **ISO zonal loads + forecasts** via gridstatus for finer geography (e.g., PJM zones, NYISO zones).
+- **ARPA-E PERFORM** load actuals + probabilistic forecasts (Section 6) for risk-aware work.
+
+### 5.3 Building/feeder level (if you need it) ◻︎
+
+**BuildingsBench** (NREL): ~900K simulated buildings (Buildings-900K) plus curated real building datasets for short-term load forecasting, with pretrain/eval splits designed for foundation-model work — including pretrained transformer weights for building-level forecasting. Overkill for wholesale-market questions but the standard at that scale.
+
+### 5.4 Open models for load forecasting ◻︎ (tiered)
+
+Load is the most mature target: models are simpler, and the open baselines are genuinely strong.
+
+**Tier 1 — Classic regression benchmarks.** The canonical open model is Tao Hong's **"vanilla benchmark"** from the GEFCom lineage: multiple linear regression on calendar effects (hour, weekday, month) crossed with polynomial temperature terms (T, T², T³ and their interactions with hour/month). Trivially reproducible, published in full, and still the standard baseline — beating it by a meaningful margin on zonal load is harder than expected, because system load is dominated by weather and seasonality the regression already captures. Documented variants (lagged/exponentially smoothed temperature, weather-station selection, recency effects) are collected in Hong & Fan's open-access probabilistic load forecasting tutorial review (*IJF* 2016).
+
+**Tier 2 — GAMs, the operational workhorse.** Generalized additive models (smooth splines on temperature, time-of-year, lagged load) are what several system operators actually run. Open tooling: **mgcv** (R) is the standard, with well-published recipes from the French school (Goude, Obst et al.), including online/adaptive versions (Kalman-filtered GAMs, aggregation of experts) that handled the COVID demand shift well. Python-native substitute: **quantile LightGBM** on the same features, with comparable performance.
+
+**Tier 3 — Probabilistic methods from the GEFCom lineage:** quantile regression on vanilla features, quantile GBM, **temperature-scenario simulation** (feed many historical weather years through a point model to obtain a predictive distribution — probably the highest value-per-line-of-code trick in open load forecasting), and sister-forecast combination. All published in reproducible detail with pinball-loss evaluation.
+
+**Tier 4 — Deep learning.** Same frameworks as prices (neuralforecast: N-HiTS/TFT/PatchTST; GluonTS: DeepAR; Darts). Deep models help most at finer granularity (nodal/feeder, many related series learned jointly) and matter less at BA/system level where weather regression dominates.
+
+**Tier 5 — Foundation models: load is their best subject.** System load is smooth, strongly seasonal, and heavily represented in pretraining corpora, so Chronos, TimesFM, Moirai, TiRex et al. do genuinely well zero-shot; several 2024–2026 studies find TimesFM competitive with or better than task-trained models, especially with long contexts. Two caveats: **leakage** (public load datasets sit in the pretraining corpora, so public-benchmark wins overstate real-world skill) and **covariates** (vanilla zero-shot use ignores weather forecasts, where much of the operational skill lives — covariate-capable variants like Chronos-2, or fine-tuning, close that gap).
+
+**Operational bar:** ISO/BA published DA load forecasts (via gridstatus, or EIA-930 which carries every BA's DA demand forecast alongside actuals) typically run ~1–2% MAPE day-ahead at system level. A model that doesn't beat the published forecast on a proper out-of-sample window isn't adding value — a test that a surprising share of published deep-learning results quietly fails.
+
+---
+
+## 6. Wind, solar, and the "net load" drivers
+
+### 6.1 ARPA-E PERFORM datasets (NREL) ✅ — closest thing to a modern US market forecasting benchmark
+
+**Portal:** https://data.openei.org/submissions/5772 • **Docs:** https://github.com/PERFORM-Forecasts/documentation • DOI: 10.25984/1891136
+
+Time-coincident **load, wind, and solar actuals plus deterministic and probabilistic forecasts** at 5-minute resolution for **ERCOT, MISO, NYISO, and SPP**, at site, zone, and BA level. Coverage: ERCOT actuals 2017–2018 with forecasts for 2018; MISO/NYISO/SPP actuals 2018–2019 with forecasts for 2019. Wind/solar profiles include both existing sites and planned sites from interconnection queues (as of 2021). Distributed as HDF5 on AWS S3 (public), timestamps in UTC; NREL technical reports document methods and error metrics.
+
+This is now a common substrate in the literature for evaluating probabilistic forecasting, conformal calibration, and foundation models on realistic US grid data. Limitations: fixed vintage (2017–2019), synthetic-but-validated actuals for renewables, no prices — you can pair it with historical LMPs from Section 2 at your own risk of misalignment.
+
+### 6.2 Resource datasets ◻︎
+
+- **NREL NSRDB** (solar irradiance, 4-km half-hourly, 1998–present) and **WIND Toolkit** / **WTK-LED** (wind resource) — free via NREL APIs/OEDI.
+- **ISO-published renewable forecasts** (ERCOT STWPF/STPPF, CAISO, SPP, MISO) via gridstatus — operational baselines.
+- **Solar/wind competition datasets**: GEFCom2014 tracks; more recent Kaggle/CityLearn-style sets exist but none is US-wholesale-specific.
+
+### 6.3 Open models for renewable output forecasting ◻︎ (tiered)
+
+Renewables forecasting is the most physics-flavored target. The canonical chain is **weather forecast → power conversion → statistical correction**, with open components at every stage; the right model family depends primarily on horizon.
+
+**Tier 1 — Physical model chains (NWP → power), the day-ahead workhorse.** Take an NWP forecast (HRRR for CONUS; ECMWF/GFS for longer horizons) and convert to power:
+
+- **pvlib-python** — the canonical open PV modeling library (NREL/Sandia lineage): irradiance transposition, temperature and inverter models; the backbone of most open solar stacks.
+- **PySAM / SAM** (NREL) — full-plant simulation including tracking, losses, storage.
+- **windpowerlib** and **PyWake** (DTU) — hub-height wind to turbine/farm power with wake modeling, using open turbine/power-curve databases.
+
+Pure physical chains carry bias (plant metadata is never quite right), so operational systems wrap them in a statistical correction:
+
+**Tier 2 — Statistical/ML correction and direct ML.** The dominant practical pattern: gradient-boosted trees (LightGBM/XGBoost) mapping NWP features (multi-member, multi-model wind/irradiance at several grid points and heights, plus solar geometry from pvlib) directly to plant or fleet output — the approach that won most GEFCom2014 wind/solar tracks and remains state of the art at hours-to-days horizons. Quantile versions give calibrated probabilistic output. The **analog ensemble** (find historically similar forecast conditions; use the distribution of realized outputs) is a well-published, easily reproduced probabilistic alternative.
+
+**Tier 3 — Nowcasting (minutes to ~4 hours), a genuinely distinct problem.** NWP is stale at these horizons; skill comes from recent observations and satellite imagery. **Smart persistence** (persist the clear-sky index for solar, not raw power) is the standard baseline and hard to beat inside ~1 hour. **Open Climate Fix** is the notable fully open effort: PVNet forecasts solar generation from satellite imagery + NWP with open code and open weights on Hugging Face, running operationally for Great Britain's system operator — the architecture transfers to US data though pretrained weights are GB-centric. Deep learning on satellite/sky-camera sequences (ConvLSTM/transformer variants) has abundant published code but no dominant packaged model.
+
+**Tier 4 — AI weather models (the big recent shift).** Open-weights ML weather models — **GraphCast/GenCast** (DeepMind), **Pangu-Weather** (Huawei), **FourCastNet** (NVIDIA), **Aurora** (Microsoft), plus ECMWF's **AIFS** — rival or beat physical NWP on many variables at a fraction of the compute. For renewables they matter mainly as a cheap way to generate large ensembles of the *inputs* (wind fields, radiation), upgrading the probabilistic layer downstream. Radiation and hub-height winds remain weaker than temperature/pressure — validate before swapping them in for HRRR.
+
+**Practical guidance.** (1) The leakage rule applies doubly: evaluate against archived *forecasts* as issued, never reanalysis-derived weather, or backtests will flatter you. (2) Fleet-level forecasting (what moves prices) is much easier than single-plant — errors decorrelate across sites — so match aggregation level to use case; a mediocre per-plant model can be excellent for ERCOT-wide net load. (3) For wholesale-market purposes the quantity you usually want is **net load** (load − wind − solar) and its ramps, the driver of RT prices: forecast the three components separately with the tools above, combine, and benchmark against the ISO's implied net-load forecast. Baselines to beat: each ISO's published wind/solar forecasts (ERCOT STWPF/STPPF, CAISO, SPP — via gridstatus) and smart persistence at short horizons.
+
+---
+
+## 7. Capacity: installed base and the pipeline
+
+"Capacity forecasting" in practice means tracking the installed fleet and projecting additions/retirements. The open data here is excellent even though there's no ML benchmark:
+
+- **EIA-860 / 860M** ✅ (via EIA or, cleaned, via PUDL): unit-level installed capacity, technology, and **planned additions and retirements** with expected online dates; 860M updates monthly. This is the baseline capacity forecast used across the industry.
+- **LBNL "Queued Up" + interconnection queue dataset** ✅: Berkeley Lab (with interconnection.fyi) compiles project-level interconnection queue data from >50 grid operators (7 ISO/RTOs + ~50 non-ISO BAs, ≈98% of US installed capacity). Latest data file covers requests **through end of 2025** (over 2,060 GW of active generation + storage capacity; gas up 86% YoY to 253 GW, solar 773 GW, storage 749 GW, wind 220 GW; 549 GW already with draft/executed interconnection agreements). Includes a codebook and dozens of summary tables. The accompanying PDF analysis ("Queued Up: 2025 Edition") covers data through 2024. Key modeling fact from the report: historically **only ~13% of queued capacity (2000–2019 requests) reached commercial operation**, and median IR→COD duration now exceeds 4–5 years — so queues need survival-rate modeling, not face-value use. Download: https://emp.lbl.gov/queues
+- **gridstatus interconnection-queue scrapes** ✅ for the live per-ISO queues between annual LBNL releases.
+- **EPA CEMS** (via PUDL) for realized unit-level operation; **EIA-411/NERC ES&D** ◻︎ for reserve-margin/resource-adequacy projections; ISO planning reports (ERCOT CDR, PJM load forecasts, CAISO IRP) ◻︎ for the ISOs' own capacity outlooks.
+
+---
+
+## 8. Generic time-series benchmarks and foundation models (context you'll bump into)
+
+- **GIFT-Eval** ✅ (Salesforce; arXiv:2410.10393): the current standard general forecasting benchmark — 97 tasks from ~23–55 datasets, 144K series, 7 domains including Energy, with an explicit pretraining corpus and leaderboard. Recent foundation models (Chronos-2, TimesFM-2.5, TiRex, Toto, Moirai-2) report on it. Its energy data is building/consumption-flavored (UCI Electricity, BDG-2, ETT…), **not** US wholesale prices — so treat GIFT-Eval rank as weak evidence for LMP-forecasting skill.
+- **Leakage warning** ✅: GIFT-Eval's authors note partial pretraining leakage for TimesFM/Chronos/Moirai on common public sets, and load-forecasting benchmark papers disqualify closed-data models (e.g., TimeGPT-1) for unverifiable training data. If you benchmark foundation models on public ISO/EIA data, assume contamination unless the model documents its corpus.
+- Other useful open evaluation stacks: Nixtla's **statsforecast/neuralforecast/utilsforecast**, **GluonTS**, **Darts**, **sktime**, and Monash Forecasting Archive ◻︎.
+
+---
+
+## 9. Structural and market-simulation models
+
+Pure time-series models extrapolate from history and therefore struggle with regime changes: new transmission, generator retirements, fuel shocks, major outages, storage and renewable additions, or revised scarcity rules. Open structural tools provide a complementary view:
+
+- **Prescient** — Python production-cost framework for unit commitment, economic dispatch, and sequential simulation, with RTS-GMLC examples.
+- **Sienna / PowerSimulations.jl** — Julia framework for sequential operations, unit commitment, dispatch, and scenario studies.
+- **PyPSA** — economic dispatch, unit commitment, linear optimal power flow, transmission modeling, and capacity expansion.
+- **QuESt PCM** — Sandia's Python production-cost model with detailed storage representation.
+- **PyPSA-USA** — configurable US transmission, production-cost, capacity-expansion, and regional-scenario model.
+- **PowerGenome** — builds harmonized US generator fleets, load profiles, regional aggregations, and technology assumptions from public data.
+
+A useful hybrid architecture is:
+
+1. A structural model estimates system lambda, dispatch, reserves, and congestion state.
+2. A statistical or ML model corrects systematic structural-model error.
+3. A probabilistic layer produces calibrated intervals or scenarios.
+
+This is most valuable when the forecast period differs materially from the training period. For controlled experiments, use an open synthetic network such as RTS-GMLC; do not imply that synthetic-grid results establish live nodal-market performance.
+
+---
+
+## 10. Recommended benchmark design
+
+A credible open US benchmark should separate tasks rather than collapse unlike targets into one score:
+
+| Task | Horizon | Target |
+|---|---:|---|
+| DA hub or zonal price | Next 24 hours | Hourly LMP, SPP, or LBMP |
+| RT price | 1–24 hours | 5-minute or 15-minute price |
+| DART spread | Next operating day | RT minus DA |
+| Nodal congestion | Next operating day | Congestion component or binding probability |
+| Load | 5 minutes–7 days | System and zonal MW |
+| Available capacity | 1–7 days | Available and forced-outage MW |
+| Renewable availability | 5 minutes–2 days | Wind or solar potential output |
+| Scarcity event | 1–48 hours | Reserve-shortage or price-spike probability |
+
+### 10.1 Evaluation protocol
+
+- Use rolling-origin backtests, never random train/test splits.
+- Enforce strict as-of availability and preserve every forecast vintage, publication time, revision time, and delivery time.
+- Handle market calendars, daylight-saving transitions, and interval-ending conventions explicitly.
+- Report results by season, hour, node liquidity, congestion regime, and normal versus scarcity periods.
+- Include extreme-weather windows rather than allowing average periods to dominate the score.
+- Compare against the ISO's published forecast where one exists, plus naive seasonal and problem-specific baselines.
+
+### 10.2 Metrics
+
+For point forecasts, report MAE, RMSE, MASE, and target-specific peak or ramp error. Avoid MAPE for power prices because prices can be zero, near zero, or negative.
+
+For probabilistic forecasts, report pinball loss or CRPS, prediction-interval coverage, and calibration. Use energy score when evaluating multivariate scenarios. For scarcity, congestion, and ramp events, add precision, recall, PR-AUC, and a lead-time- or cost-weighted event score.
+
+Use Diebold-Mariano or Giacomini-White tests, block bootstrap, and regime-specific comparisons where appropriate; a small metric improvement without statistical or economic significance is not enough.
+
+### 10.3 Economic evaluation
+
+Price forecasts should also be tested in a fully specified decision simulation. Include bid timing, position and volume limits, transaction costs, slippage, fees, price caps and floors, settlement rules, risk-adjusted P&L, drawdown, and tail performance. Keep the forecasting model comparison separate from the trading-policy comparison so improvements are attributable.
+
+### 10.4 Modeling invariants
+
+1. **Preserve forecast vintages.** Never train with realized or revised weather when the operational model would have seen an earlier forecast.
+2. **Separate ordinary and extreme regimes.** Report both; do not let common hours hide failure on high-value events.
+3. **Forecast components where useful.** Decompose LMP into energy, congestion, and loss components; renewable output into potential, availability, and curtailment; net load into load, wind, and solar.
+4. **Use global models carefully.** Cross-node or cross-market training can help sparse series but requires identifiers, geography, market structure, and per-series reporting.
+5. **Evaluate operational usefulness.** Statistical accuracy, calibration, latency, stability, and economic value are distinct requirements.
+
+---
+
+## 11. Known gaps and paywalls (so you don't discover them the hard way)
+
+1. **No maintained modern US nodal DA/RT price benchmark** with aligned, as-of-time features. Everyone rebuilds; results across papers are rarely comparable. (Verified gap; also called out in the EPF literature itself.)
+2. **Natural gas hub prices** — the single most important fuel driver — are the binding data constraint. Henry Hub (EIA, free, daily) is insufficient for ERCOT/PJM/New England, which price off **Waha, TETCO M3, Algonquin Citygates**, etc.; those daily indices are commercial (Platts, NGI, ICE). Partial free workarounds: EIA weekly/monthly regional prices, futures settlements, and lagged proxies — all materially degrade winter price models in gas-constrained regions. ◻︎
+3. **As-of-time weather forecasts**: reanalysis leakage is the most common silent bug in published load/price models; use archived HRRR/GFS.
+4. **Network models/topology** are confidential (CEII); nodal congestion forecasting must rely on learned patterns or synthetic grids (Section 9: RTS-GMLC, Texas A&M synthetic cases like the Texas 7k, Breakthrough Energy's US test system, PGLib-OPF, with open tooling in PyPSA-USA, NREL Sienna, Egret) ◻︎.
+5. **ISO data licensing**: data is free to access but some ISOs restrict redistribution — one more reason public benchmarks are scarce. Check each ISO's terms before republishing datasets. ◻︎
+
+---
+
+## 12. Recommended starting stacks (by problem)
+
+**DA zonal/hub price forecasting (research-grade, comparable):**
+epftoolbox (models + tests + PJM/ComEd set) for methodology → rebuild a modern dataset via gridstatus (DA LMPs, ISO load + renewable forecasts) for 2021→present → report rMAE vs naive + LEAR + DNN, DM/GW tests, pinball/CRPS if probabilistic.
+
+**RT nodal / DART spread:**
+gridstatus RT+DA LMPs at chosen nodes; features: ISO forecasts, outage reports, net-load ramps, gas proxy, DA prices; two-stage (spike classifier + magnitude regressor) is the common open pattern; evaluate economically (trading P&L of virtual bids) as well as statistically.
+
+**Load:**
+EIA-930 (cleaned) or ISO zonal loads; baseline = the BA/ISO published forecast; GEFCom2014-style quantile evaluation; HRRR weather features via herbie/Open-Meteo.
+
+**Wind/solar & risk-aware operations:**
+ARPA-E PERFORM as the benchmark substrate; NSRDB/WIND Toolkit for resource; compare against ISO operational forecasts.
+
+**Capacity outlooks:**
+EIA-860M (planned units) + LBNL queue data with historical completion rates by region/technology/queue-vintage as survival priors; PUDL for the installed-fleet backbone.
+
+---
+
+## 13. Link index
+
+| Resource | URL |
+|---|---|
+| gridstatus (library) | https://github.com/gridstatus/gridstatus |
+| epftoolbox (EPF benchmark) | https://github.com/jeslago/epftoolbox |
+| Lago et al. 2021 (open access) | https://doi.org/10.1016/j.apenergy.2021.116983 |
+| EIA API / Hourly Grid Monitor | https://www.eia.gov/opendata/ |
+| Cleaned EIA-930 demand | https://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Data |
+| PUDL | https://catalystcoop-pudl.readthedocs.io |
+| ARPA-E PERFORM datasets | https://data.openei.org/submissions/5772 |
+| PERFORM documentation | https://github.com/PERFORM-Forecasts/documentation |
+| LBNL Queued Up / queue data | https://emp.lbl.gov/queues |
+| NREL NSRDB | https://nsrdb.nrel.gov |
+| NREL WIND Toolkit | https://www.nrel.gov/grid/wind-toolkit |
+| GEFCom archives | http://www.drhongtao.com/gefcom |
+| GIFT-Eval | https://arxiv.org/abs/2410.10393 |
+| Chronos | https://github.com/amazon-science/chronos-forecasting |
+| TimesFM | https://github.com/google-research/timesfm |
+| Nixtla forecasting libraries | https://github.com/Nixtla |
+| CAISO OASIS | http://oasis.caiso.com |
+| PJM Data Miner 2 | https://dataminer2.pjm.com |
+| ERCOT public API | https://data.ercot.com |
+| NYISO archives | https://mis.nyiso.com/public/ |
+| RTS-GMLC (synthetic grid) | https://github.com/GridMod/RTS-GMLC |
+| Texas A&M synthetic grids | https://electricgrids.engr.tamu.edu |
+| PyPSA-USA | https://github.com/PyPSA/pypsa-usa |
+| Prescient | https://github.com/grid-parity-exchange/Prescient |
+| Sienna | https://sienna-platform.github.io/Sienna/ |
+| PyPSA | https://pypsa.org/ |
+| QuESt PCM | https://www.sandia.gov/ess/tools-resources/quest/quest-pcm |
+| PowerGenome | https://github.com/PowerGenome/PowerGenome |
+| Herbie (HRRR access) | https://github.com/blaylockbk/Herbie |
+| Open-Meteo | https://open-meteo.com |
+| pvlib-python | https://github.com/pvlib/pvlib-python |
+| PySAM (NREL) | https://github.com/NREL/pysam |
+| windpowerlib | https://github.com/wind-python/windpowerlib |
+| PyWake (DTU) | https://github.com/DTUWindEnergy/PyWake |
+| Open Climate Fix (PVNet et al.) | https://github.com/openclimatefix |
+| GluonTS | https://github.com/awslabs/gluonts |
+| Darts | https://github.com/unit8co/darts |
+| mgcv (GAMs, R) | https://cran.r-project.org/package=mgcv |
+| Hong & Fan, probabilistic load forecasting review | https://doi.org/10.1016/j.ijforecast.2015.11.011 |
+
+---
+
+## 14. Verification notes
+
+Items marked ✅ were confirmed against primary sources in July 2026 (GitHub repos, OEDI/OSTI records, LBNL publication pages, journal/arXiv papers). Items marked ◻︎ reflect well-established domain knowledge but were not individually re-verified for this report. The tiered model sections (4.4, 5.4, 6.3) are ◻︎ overall: the existence and character of each listed model/library is standard knowledge, and the epftoolbox specifics within them are ✅, but claims about *relative performance* (e.g., LEAR/GBM vs. foundation models on spiky prices, TimesFM competitiveness on load) summarize the 2024–2026 literature's general findings and will shift as models evolve — re-check against current papers before citing — in particular, exact ISO portal URLs/registration requirements and commercial gas-index licensing change periodically and should be checked at time of use. Dataset vintages stated (PERFORM 2017–2019; epftoolbox ending 2018; cleaned EIA-930 through Jan 2025; LBNL queues through end-2025) are as published at compile time.