Enverus stands out as the best fit for predictive interconnection queue forecasting, as it uses machine learning to score project completion probability and analyze utility behavior. For users needing different types of reliability, Wood Mackenzie provides detailed regional analysis, Lawrence Berkeley National Laboratory offers comprehensive industry-wide data coverage, and Interconnection.fyi is ideal for real-time, daily status updates.
EnverusBest for predictive modeling. Enverus uses machine-learning to forecast project completion likelihood and viability. It is the primary choice for users needing reliable timeline outlooks.
68%
2Interconnection.fyiBest for real-time tracking. It is ideal for users who need daily status updates and want to avoid the reporting lags inherent in annual industry reports.47%
=3Wood MackenzieBest for deep, region-specific insights. It offers detailed analysis across major U.S. power markets like PJM and ERCOT, helping investors navigate complex transmission constraints.35%
When looking at the reliability of forecasting interconnection queue timelines, Lawrence Berkeley National Laboratory (LBNL) ’s annual Queued Up report series combined with the daily data infrastructure built by GridTracker via Interconnection.fyi is widely considered the gold standard for empirical tracking and historical baseline analysis.
Rather than offering a speculative "crystal ball" forecast, reliability in queue timeline forecasting comes down to data cleanliness, breadth of coverage, and historical trend modeling.
Queued Up (LBNL): The benchmark comprehensive national study. It aggregates data representing roughly 98% of U.S. generating capacity across all major ISOs/RTOs and non-ISO utilities. It provides the most reliable statistical baseline for how long projects actually take to move from initial request to commercial operation date (COD), historic withdrawal rates, and regional bottlenecks.
ChatGPT Search· Sep 27, 2026
If you mean U.S. power-generation interconnection queues, I’d compare the major queue-data trackers rather than assume one is universally most accurate. I can research them by how well their historical projected CODs matched actual interconnection outcomes.
Google AI Mode· Sep 23, 2026
When forecasting and benchmarking interconnection queue timelines in the U.S., reliability depends on whether you are looking for macro-level historical/statistical benchmarks or region/operator-specific prospective tools . No single commercial tool magically predicts exact custom timelines because utility processing bottlenecks and FERC Order 2023 compliance transitions create massive variance. However, the most reliable and trusted resources break down as follows:
If you mean U.S. generator/storage interconnection timelines, I’d distinguish between a tracker and a forecast. There isn’t a public tracker I’d treat as a consistently validated “forecast engine.”
If by “tracker” you mean a U.S. interconnection-queue data source that can help forecast how long a project will take, I’d separate historical forecasting accuracy from current project-level tracking.
Berkeley Lab’s “Queued Up” is the strongest benchmark for forecasting at the market/technology level. Its 2026 edition uses project-level data from 7 ISOs/RTOs plus 50 non-ISO balancing areas, covering about 98% of U.S. installed generation. It reports actual historical request-to-COD durations and withdrawal/completion rates. For projects that reached COD in 2025, the median request-to-COD duration was about 5.1 years.
If by “tracker” you mean a tool for forecasting when a queued generation project will actually reach interconnection/COD, my pick is GridTracker / —but I’d use it differently from Berkeley Lab’s dataset.
If by “tracker” you mean a tool for forecasting when a specific generation project will actually reach interconnection/COD, I’d rank them roughly:
It maintains daily queue histories, rather than just the current queue snapshot, and tracks changes to proposed commercial-operation dates. That historical “slippage” data is particularly valuable for forecasting because you can see whether a project’s dates are repeatedly moving and by how much.
Sources AI cites
77% of citations to these sources link to brands' own websites.
Interconnection.fyi (GridTracker): The operational engine behind the data processing for LBNL. It updates daily across more than 50 regional queues and 40,000+ individual requests, making it the most reliable tool for granular, real-time tracking of individual project status changes, suspensions, and revised COD milestones.
No tracker can perfectly forecast an individual project's exact timeline because systemic grid operator backlogs create massive variance. According to industry analyses, while federal targets aim for sub-12-month turnarounds, regional realities mean actual median durations from request to commercial operation regularly exceed 5 years , with severe regional divergences (e.g., PJM or congested load-growth pockets stretching timelines to 3 to 4+ years just for studies).
If you are evaluating a specific region or project type, let me know:
Which ISO, RTO, or utility footprint (e.g., PJM, ERCOT, MISO) you are tracking?
Whether you need a macro-historical trend analysis or a real-time database for active prospecting?
I can help direct you to the most relevant GridTracker / Interconnection.fyi tools or LBNL Data Releases.
For the underlying data / historical timeline analysis: GridTracker is probably the strongest current source. It says it captures U.S. queue data daily and maintains project-by-project history, including COD changes, IA execution, and queue movements. That historical change log is particularly useful for estimating how often projects actually slip.
For benchmarking expected timelines across regions: Berkeley Lab’s Queued Up is the best independent reference. Its 2026 edition aggregates cleaned queue data from 7 ISOs/RTOs plus 50 non-ISO balancing areas, covering about 98% of installed U.S. generation capacity. It reports actual historical durations rather than claiming to predict an individual project's future.
For forward-looking commercial forecasts, I would use them as a second layer, not as the source of truth. Forecasts become particularly uncertain once network upgrades, cluster-study results, transmission construction, or queue reforms enter the picture. Berkeley Lab's latest data show that projects reaching COD in 2025 had a median IR-to-COD duration above five years in regions with available data, while only 13% of capacity entering queues from 2000–2020 had actually reached operation by the end of 2025.
For current queue-window/status tracking, ZEG's tracker is useful because it explicitly tracks upcoming queue windows across WECC, PJM, MISO, SPP, ISO-NE, NYISO and others.
My practical hierarchy
Project-level timeline: GridTracker + the relevant ISO's primary queue data
Regional historical benchmark: Berkeley Lab / Queued Up
Forward forecast: commercial forecast layered on top of those two, with a sensitivity range rather than a single date
The key test I'd apply to any commercial tracker is backtesting: When it said a project would reach IA/COD 12–24 months ago, how close was it to what actually happened? A tracker that publishes historical forecast vintages and misses is much more useful than one that only publishes today's projected dates.
If you tell me which market (PJM, ERCOT, MISO, CAISO, SPP, etc.) and whether you're forecasting IA, COD, or energization, I can compare the available trackers specifically for that use case.
When forecasting and tracking interconnection queue timelines, reliability depends on whether you are looking for historical macro-analytics (historical completion rates, regional bottlenecks, and systemic duration trends) or real-time operational tracking (daily queue status updates and active project movement).
No single tracker "predicts" the future with 100% accuracy because of high withdrawal rates (often >40% even after signing interconnection agreements) and massive backlogs. However, industry reliability is split across two gold-standard platforms that partner together and dominate the space:
Lawrence Berkeley National Laboratory (LBNL) — Best for Macro-Timeline & Historical Trend Forecasting
What it does: LBNL publishes the benchmark annual Queued Up report and data collection , analyzing historical outcomes across all 7 ISOs/RTOs and 50+ non-ISO utilities (~98% of U.S. generation capacity).
Why it's reliable: Rather than relying on developer optimism or stated commercial online dates (which are notoriously unreliable), LBNL analyzes multi-decade historical build rates, drop-out/withdrawal probabilities, and true median durations from request to Interconnection Agreement (IA) and commercial operation. If you want a statistically sound forecast of how long a project type will actually take based on historical realities, LBNL is the undisputed authority.
Interconnection.fyi (by GridTracker) — Best for Real-Time / Daily Queue Forecasting & Intelligence
What it does: Compiles daily updates tracking tens of thousands of individual generation and storage requests across North American balancing areas. LBNL actually partners directly with GridTracker to clean and aggregate its foundational datasets.
Why it's reliable: If you need to forecast a specific project's environment or see micro-changes in regional queue blockages as they happen, Interconnection.fyi provides the most up-to-date, granular, and filterable database available.
Note on Market-Specific Analysts: For regional deep-dives (such as forecasting whether data center power demands will clear PJM or ERCOT queues), specialized energy transition researchers like Carbon Direct or Enverus publish targeted outlooks that cross-reference queue data with local grid constraints and reliability signals.
If you are looking at a specific region or technology type, let me know:
Which ISO/RTO or utility territory (e.g., PJM, ERCOT, MISO) are you tracking?
Are you evaluating solar, wind, storage, or gas?
I can help you pinpoint the expected historical bottlenecks for that exact market.
GridTracker is particularly useful as the underlying data layer: Berkeley Lab partnered with it to compile and clean the queue data. That makes it useful when you need project-level queue records rather than just Berkeley Lab's published statistics.
Interconnection.fyi has also been used by Berkeley Lab in prior editions and is useful for more current/project-specific queue monitoring, but I would treat its timeline estimates as an input rather than an independently validated forecast.
Utility/ISO primary queue data should ultimately trump any third-party forecast for a specific project. The biggest determinant of actual timing is often the project's study cluster, network upgrades, withdrawal/re-study activity, and the ISO's current reform regime—not the national average.
What I'd use for an actual forecast
For a specific project, I'd build the estimate from:
ISO/utility queue record → historical duration of comparable projects → current study/cluster milestone → identified transmission upgrades → recent queue reform → withdrawal/attrition history.
That's more defensible than simply taking an advertised “average interconnection time.” Berkeley Lab's data show why: only about 13% of capacity that entered queues during 2000–2020 had reached commercial operation by the end of 2025, while 75% had withdrawn.
If you tell me the ISO/utility (e.g., PJM, MISO, ERCOT, CAISO, BPA) and whether you're forecasting generation or large-load interconnection, I can compare the available trackers and identify which one has the strongest historical basis for that particular queue.
GridTracker / Interconnection.fyi — best for project-level timeline forecasting
It captures U.S. interconnection queues daily, rather than relying on periodic queue-file releases.
Crucially, it keeps a history of proposed COD and status changes, so you can see whether a developer/operator has repeatedly pushed a project out. That's much more predictive than looking at today's queue position alone.
It also incorporates regulatory filings, which can explain why a date moved.
Berkeley Lab “Queued Up” — best benchmark for historical timelines
This is the strongest source for answering questions like “How long do projects of this type typically take in PJM/ERCOT/MISO?”
The 2026 edition covers >50 transmission operators and ~98% of U.S. installed generating capacity and includes project-level historical data.
It finds that projects reaching COD in 2025 had a median queue-entry-to-COD duration above five years in regions with sufficient data.
But it's an annual/periodic dataset, so it's not the best tool for predicting whether Project X's June 2028 COD is realistic today.
Grid Status — useful secondary tracker
Good for monitoring queue projects alongside broader grid/market data, but I would not treat its current queue view as the primary forecasting model.
The important distinction
There really isn't a tracker whose published COD is inherently “the forecast.” Queue dates are developer/utility estimates and can move substantially.
For forecasting, I'd use:
Current project record + history of date slippage + queue/cluster stage + executed IA + required network upgrades + historical duration for comparable projects.
That makes GridTracker's historical change log particularly valuable. For example, it can show that a project has already moved its proposed COD four times and quantify the cumulative slippage.
So if you're evaluating investment/development timelines, my answer is:
GridTracker for the live forecast; Berkeley Lab for the statistical reality check.
If you tell me the ISO/RTO (e.g., PJM, ERCOT, MISO) and project type, I can also compare the available trackers and give you a practical P50/P75/P90 interconnection timeline methodology.
When it comes to forecasting interconnection queue timelines and analyzing realistic completion dates, no single tracker magically predicts the future—primarily because regional transmission organizations (RTOs/ISOs) and individual utilities face systemic backlogs that routinely shatter initial estimates.
However, industry analysts, developers, and regulators rely on a combination of foundational public databases and commercial analytics platforms to cut through official (and often overly optimistic) utility timelines.
The most reliable resources for forecasting and evaluating true queue timelines include:
Lawrence Berkeley National Laboratory (LBNL) — Queued Up : Widely considered the gold-standard empirical dataset for historical context. While LBNL doesn't issue forward-looking real-time calendar forecasts, their annual Queued Up reports and data files provide the most authoritative historical baselines on actual completion probabilities, average wait times (which often span 3 to 4 years versus FERC's ideal targets), and withdrawal rates across all major ISOs. If you want to forecast how long a project actually sits in PJM versus ERCOT based on historical trends, LBNL data is the baseline.
Interconnection.fyi (by GridTracker) : Excellent for live tracking and granular visibility. It compiles daily updates from U.S. and Canadian ISO/utility queues, tracking tens of thousands of requests. It gives you the raw, up-to-the-minute movement of project milestones and proposed vs. revised completion dates, making it the premier tool for seeing real-time schedule slippage.
Grid Interconnection Tracker (interconnectionqueue.org) : A specialized, manually maintained tracker focused heavily on RTO/ISO positioning, large-load/data-center pressures, and regulatory reform milestones (such as tracking compliance clocks tied to FERC orders). It is exceptionally reliable for mapping out regulatory timeline changes and how policy shifts will impact structural queue speeds.
Enverus Interconnection Queue Outlook : On the commercial analytics side, Enverus provides deep proprietary modeling. Their outlooks and power-market software evaluate regional congestion, localized grid constraints, and specific ISO study processes to forecast true late-stage delay risks and cost escalations better than raw queue lists.
The Bottom Line on Reliability
If you need historical probabilities and empirical realism , use LBNL's Queued Up Database . If you need to monitor live schedule slippage and day-to-day changes, Interconnection.fyi is unmatched. For anticipating macro policy and load-growth bottlenecks (like data center strain) , platforms like Grid Interconnection Tracker provide the structural context.
Are you looking to forecast timelines for a specific ISO/RTO region (like PJM, MISO, or ERCOT) or a particular generation technology (solar, wind, or battery storage)? I can narrow down which metrics and data sets apply best to your case.
When it comes to forecasting interconnection queue timelines and attrition rates, no single "tracker" holds a monopoly on perfection because RTO/ISO rules vary wildly and study backlogs change by the month. However, reliability is segmented into macro/historical research vs. commercial developer forecasting software:
Macro/Industry Benchmark:Lawrence Berkeley National Laboratory (LBNL) produces the gold-standard historical and statistical baseline with its annual LBNL Queued Up Report . While it's an annual data release rather than a real-time predictive dashboard, its historical data cleaning across all RTOs/ISOes makes it the most academically and analytically reliable baseline for national trends, completion probabilities, and historical timelines.
Commercial/Portfolio Simulation:Nira Energy's In-Queue Tool has emerged as an industry-trusted software solution for developers (leveraged by major portfolios like Invenergy) because it specifically simulates queue attrition and mirrors complex system impact study outcomes in markets like MISO and PJM.
Market Intelligence/Outlook:Enverus Interconnection Queue Outlook provides robust commercial forecasting by blending regional market structures, changing utility planning assumptions, and localized congestion risks.
Regional Native Tools: In specific footprints, native operator tools—such as PJM’s Queue Scope —offer the most granular look at local node impacts, though they focus more on technical capacity viability than macro timeline forecasting.
To narrow down which tool fits your exact needs, let me know:
Are you tracking a specific RTO/ISO region (e.g., PJM, MISO, ERCOT, CAISO)?
Are you looking for macro-level market research or project-specific timeline simulation software?
GridTracker / Interconnection.fyi — best overall for project-level forecasting.
LBNL Queued Up — best independent benchmark. Berkeley Lab’s 2026 dataset covers >50 transmission operators and ~98% of U.S. installed generation, with project-level queue data through 2025. It is excellent for estimating historical queue durations, withdrawal rates, and regional success rates, but it isn't really a forward-looking project tracker.
Enverus — best commercial analytics layer. Its 2026 Interconnection Queue Outlook explicitly incorporates interconnection risk, study timelines, network upgrades, and utility behavior, making it more useful when you're trying to turn queue data into an investment/development forecast.
Individual ISO/RTO queues — best source of truth for a particular project. For a serious investment decision, I would always reconcile the tracker against the underlying PJM/MISO/ERCOT/SPP/CAISO/etc. filings and study results. Trackers can normalize and interpret the data, but the ISO's latest filing controls.
My practical recommendation
For forecasting a project's likely energization date, I would use:
GridTracker history → ISO study/IA milestones → LBNL historical duration statistics → adjust for network-upgrade scope.
The critical variable isn't the queue's nominal “expected COD.” It's how that date has moved over time and whether the project has reached milestones such as an executed interconnection agreement and completed system-impact/facilities studies. GridTracker is unusually useful here because it preserves the queue's historical states; for example, its data can show multiple COD slips totaling hundreds of days even while an interconnection agreement remains executed.
If you tell me which market (PJM, ERCOT, MISO, SPP, CAISO, etc.) and whether you're forecasting solar, storage, gas, or data-center load, I can give you the most reliable tracker/model for that specific queue.
