Sunairio Announces Grid Status Data Partnership to Streamline Power Trading and Grid Risk Mitigation
Energy market participants can now seamlessly track forecast performance, backtest strategies, and navigate grid volatility in one ecosystem.
Baltimore, Md. — July 28, 2026 —Sunairio, pioneer of next-generation grid forecasting software, today announced its newest data partner, power grid data and analytics firm Grid Status. Grid Status, which monitors the real-time status of the US electric grid, will now provide independent system operator (ISO) data for Sunairio’s forecasting platform, ensuring up-to-date, complete, and reliable data for critical decision making.
Thanks to the new partnership, Sunairio customers can now contextualize and validate Sunairio grid forecasts against official ISO data at scale within a single platform. This enables users to easily monitor energy forecast performance, perform trading strategy backtests, and investigate power market fundamentals through a single user interface and API.
The new capabilities provided by the partnership will better equip users to navigate an increasingly challenging weather and energy landscape. Today’s confluence of more frequent weather extremes, higher penetration of renewable energy, and increasing electricity demand have created the perfect storm for the power grid. Clear signals of power grid dynamics are vital for every company exposed to power market risk.
“Any serious forecast consumer needs high-volume and high-quality access to public forecast benchmarks and historical realizations in order to develop commercial strategies. Our new partnership with Grid Status allows Sunairo customers to do just that for all of our grid forecasts within ISO regions,” said Sunairio founder and CEO Rob Cirincione.
Cirincione went on to explain, “The importance of quality data to serve as ground truth really can’t be underestimated. Sunairio customers can now build end-to-end workflows that train, test, and validate strategies in a single platform that leverages a trusted ISO data provider. No more combining data from different sources, and no more worrying that the historical prices aren’t correct.”
Founded in 2020, Sunairio is the pioneer of award-winning, next-generation grid forecasting software that’s the first to provide integrated energy, weather, and climate insights. Sunairio helps energy traders, grid operators, utility-scale asset developers, and VPP and demand response aggregators make better commercial decisions in the face of increasing grid variability and extreme event risks. Sunairio and Sunairio ONE have received recognition from the NSF, ACP, and EPRI. For more information, please visit sunairio.com.
Fireworks Come Early: A Retrospective of the July 1-3 2026 Heat Event in PJM
In this post, we discuss the extreme weather, grid, and market dynamics that characterized the record July 4th week heatwave in PJM.
Temperature Extremes: Decent 5-day Forecasts, Near-Record Levels, Broad RTO Coverage
Similar to Winter Storm Fern earlier this year, most of the weather forecasts picked up on the extreme nature of this event about a week out. Figure 1 presents a screenshot from the Sunairio UI depicting PJM population-weighted temperature realizations and 15-day temperature forecasts from the IFS, IFS Ensemble mean, AIFS, the AIFS Ensemble mean, and Sunairio’s hourly P50 from Friday, June 26 12Z (the Friday prior to the event).
As the tooltip box within the plot shows, forecast expectations were generally very accurate (within a degree or so of realizations) for the July 1 to July 3 period, with the IFS being a bit of an outlier – more than 3 degrees too warm for the peak of the event on Wednesday July 2.
Note as well just how close this temperature index came to setting an all-time record for PJM, which is particularly impressive given how broad the PJM footprint is. As Figure 2 shows, the heat event on July 2 almost perfectly overlapped all population centers within PJM’s territory, driving demand to near-record levels as well.
Figure 1. 15-day hourly forecasts of PJM population-weighted temperature from the IFS, IFS ENS, AIFS, AIFS ENS, and Sunairio P50. Also shown are historical actual realizations and the all-time record for this weighted temperature index.
Figure 2. Sunairio High-resolution Earth Data (SHED) 2m daily max historical temperature contours for July 2, 2026. Also overlaid are the PJM RTO footprint boundary and high-resolution gridded population data.
Record Demand Levels and Critical Demand Response
The extreme heat drove zonal and RTO-wide generation to the highest levels we’ve seen in years, and approached all-time records on July 1 and July 2.
Figure 3 plots daily max population-weighted temperature versus daily max load since 2023. As the plot shows, cooling load in PJM (demand sensitivity to temperatures above 65 deg F) increases in an approximately linear fashion. Additionally, this heat wave produced three of the most extreme (“upper-right”) points.
In order to manage the associated extreme level of grid stress and ensure that generation reserves stayed above contingency requirements (basically ensuring that reserves are greater than the single largest potential unit trip), PJM activated various levels of Demand Response (DR) (120-min lead time, 60-min lead time, and 30-min lead time) throughout the day on July 2 and again on July 3. Note that PJM did NOT activate Demand Response on July 1 (more on this later).
Figure 3. Daily max PJM population-weighted temperature vs daily max PJM load, 2023-July 4, 2026
Zooming in on the cooling regime above 70 degrees and only plotting 2026 (Figure 4), we can clearly see that the linear relationship between temperature and load broke down on July 1 and 2 – but there was more than one demand-reducing dynamic to blame.
On July 2, the difference between the normal linear relationship and actual load is the effect of demand response and coincident peak shaving (commercial and industrial customers reducing their demand to lower their measured consumption in the anticipated ISO peak load interval, thereby lowering demand charges). On July 3, two factors contributed to lower demand: A) demand response/peak shaving and B) a holiday effect caused by the observance of July 4 (which fell on Saturday this year).
Regarding the magnitude of demand reduction actions on July 2 and July 3 – this chart alone implies that we observed approximately 5 GW of load reductions, and potentially up to about 7 GW on July 2.
Figure 4. Daily max temperature vs daily max load, 2026, and a linearly-fitted trend line. July 1-3 are shown along with the approximate reductions due to demand response, peak shaving, and holiday effects.
NERC Emergencies, RT Price Maximums, and a Threat to Data Centers
As the day wore on during the July 2 afternoon, reserves continued to fall in PJM even though all DR programs had been activated and real-time (RT) prices approached the $2,000/MWh offer cap. With no more market-based solutions available, PJM issued a NERC EEA 2 emergency at 5:00 PM for the entire system, indicating that reserve levels were likely to fall below required minimum levels without extraordinary interventions.
Over the next hour, PJM issued two of these extraordinary directives. At 5:32, the ISO warned of the “potential need to direct Large Loads to operate on Back-up Generation,” effectively warning any data center with backup generation that it was about to be disconnected from the grid. Although PJM did not follow up with the actual disconnect order, this is the closest the ISO has come to initiating this new emergency measure – first requested (and granted by DOE) before Winter Storm Fern.
Minutes later at 5:36 PM, PJM issued a “Deploy All Resources Action,” which ordered all generators to operate at Emergency Maximum levels.
At this point, RT prices had climbed well above $2,000 in the Mid-Atlantic sub-region.
Turning to Figure 5, we can see exactly how severe the conditions were by plotting Sunairio’s Grid Stress IndexTM, our proprietary measure of dispatchable capacity utilization, for 2026 year-to-date. As the plot of daily maximum GSI shows, July 1-3 were the tightest days on the grid in 2026, exceeding levels seen during Fern, the cold snap on February 9, and the EEA 1 alert days on May 18-19.
Curiously though, we recorded a lower overall maximum GSI level on July 3 (86.2%) vs July 1 (89.4%) even though PJM operated as if July 3 was tighter – issuing an EEA 2 emergency and activating DR.
We believe these actions can be partly explained by conservative human behavior in the control room. That is, we hypothesize that grid operators erred on the side of caution on Friday July 3 after seeing just how close the system came to reserve level violations on Thursday July 2. Moreover, the operators may have worried that any call to action (such as demand response) would yield less participation on Friday compared to Thursday given the observed holiday.
Figure 5. Daily max Grid Stress IndexTM for January 1 - July 4, 2026.
Extreme Price Risk Captured by Sunairio ONE
Putting this all together, we return to the Friday before the event (June 29) to examine how well Sunairio’s PJM price forecast captured this market risk. As Figure 6 shows, Sunairio did an excellent job of highlighting the massive market upside (here plotting the average of DA and RT Western Hub) centered on July 2.
Figure 6. Sunairio forecast of Western Hub Daily 5x16 prices, initialized June 29 12 Z. The July 2 extreme event realized at P78 of our forecast.
Conclusions
The July 1-3 2026 heat wave was reasonably well forecasted by most weather models 5 days out.
Realized temperatures soared across the PJM RTO footprint, coming within 1 degree of all-time records.
PJM activated Demand Response and issued the first widespread grid emergency (NERC EEA 2) since Winter Storm Elliott.
We observe approximately 5-7 GW of demand reduction on July 2 and July 3, encompassing DR actions and coincident peak shaving.
Sunairio’s probabilistic price forecast did an excellent job of anticipating the extreme market outcomes – faithfully predicting the upside-skewed distribution for Western Hub prices, which realized at P78 of Sunairio’s forecast from 5 days out.
From API to UI: Cracking the Code on ISO Trading Hub Price Forecasting
This week, Sunairio is officially launching our Trading Hub Price Forecast Ensemble directly in the Sunairio User Interface (first in ERCOT, other ISO regions to follow soon).
For a while now, customers have used our price forecasts via API to quantify market risk. By moving this capability into the UI, we’re making a truly probabilistic, fundamentals-driven view of ISO power prices significantly more accessible — no code required.
To mark the occasion, we want to lift the hood on our methodology and share how we cracked the code on modeling highly non-linear energy prices—even in the notoriously volatile gas basis markets of the Northeast US.
A Quick Refresher: The Grid Stress Index (GSI)
You can't accurately forecast power market prices without understanding the underlying physical reality of the grid. Traditional price models try to track this by building massive, unit-by-unit generation stacks. But in a modern grid dominated by variable renewables and unpredictable weather, that traditional approach breaks down.
Our solution is the Grid Stress Index (GSI)TM, a proprietary measure of Dispatchable Capacity Utilization (DCU). Instead of tracking thousands of individual power plants, we compress the entire grid’s supply-and-demand tension into a single, elegant metric in percentage space:
This is fundamentally a relationship between [the energy that needs to be supplied by dispatchable capacity] to [the amount of dispatchable capacity available to generate].
By mapping GSI to Delivered Spark Spreads (using a standard 6.5 HR to filter out commodity fuel noise), we uncover the true, empirical supply curve of a market, as seen in Figure 1.
Figure 1 plots GSI vs delivered North Hub delivered spark spreads in ERCOT for Dec 2025 - May 2026. What we observe is a learned market supply curve that reflects the enormously skewed behavior of most power markets: relatively controlled increases in price for much of the supply-demand regime, followed by extreme increase after an inflection point (about GSI = 75% in ERCOT).
Figure 1. The relationship between grid stress and power market prices
How the Sunairio Fundamental Price Model Works
Unlocking the relationship between grid stress and spark spreads was only step one.
Translating that relationship into highly accurate, hourly forward prices requires a clever mathematical approach. Here is how the Sunairio Fundamental Price Model operates under the hood.
1. Handling DA and RT Prices as a DA+RT Average
There are significant differences in the way that the DA market is solved compared to the RT market. For example, the RT market is fundamentally physical (physical supply must match physical demand), while the DA market is essentially a financial construct. Moreover, the DA energy market doesn’t even utilize a load forecast – it clears supply offers against demand bids.
Power markets also utilize specific products known as virtuals (virtual supply and virtual demand) that work as a converging force to drive DA and RT prices together. Over the long term, the spread between DA and RT prices is exceptionally low in most power markets, though hourly and daily spreads can be quite wide.
Given the difficulty of fully reconstructing the DA market inputs (you would have to know demand bids and exact virtuals participation), and the existence of virtuals as a converging force, Sunairio doesn’t model the DA and RT markets separately. Instead we model an average of DA and RT prices, which represent the overall economic balance for that day.
2. Temporal Bucketing
Power markets behave differently depending on the clock and the calendar. The grid dynamics of a Tuesday afternoon are vastly different from a Sunday morning – driven by both the hourly/daily cycle of demand and the physical constraints (like ramp rates and minimum run times) of many thermal generators.
For example, units with high startup costs or long minimum run times may self-schedule themselves overnight (essentially offering the plant as a market-price taker with $0 marginal cost rather than an economic resource at actual marginal costs) in order to more effectively compete for the prime daytime peak prices the next day. This means the overnight (7x8) stack is different from the weekday peak (5x16) stack.
To capture these operational nuances, we segment our historical grid data into distinct time buckets:
5x16 (Weekday peak)
2x16h (Weekend peak and holidays)
7x8 (Off-peak/night)
3. Outage Regime Bucketing
The Grid Stress Index is dependent on knowing the amount of dispatchable generation offline. While we can never know with 100% certainty exactly what units will be unavailable in the future, Sunairio has built highly accurate outage forecasts that can predict the aggregate amount of MW offline.
The challenge is that not all generation outages are made the same. Removing 100 MW of baseload power from the generation stack could affect every hour’s price (by forcing the grid operator to dispatch more expensive units to meet baseload demand), while removing 100 MW of a peaker might not affect anything if demand doesn’t rise high enough.
We can clearly observe this dynamic in the plot below, which colors the GSI vs spark spread plot for ERCOT according to the percentage of dispatchable units offline. As Figure 2 shows, the inflection point in the empirical market supply curve is shifted left (occurs at lower levels of apparent GSI) when total nonrenewable outages are greater than 25% of installed nonrenewable capacity.
Figure 2. The effect of nonrenewable (thermal) generation outages on the relationship between GSI and delivered spark spreads
4. Emulating the Stack via Isotonic Regression
Once the data is bucketed, we fit the GSI vs. Spark Spread relationship using Isotonic Regression.
Unlike standard linear regressions that force a smooth line through data points, isotonic regression specifies only one constraint: the function must be non-decreasing. This mathematical choice yields an extraordinary side effect—it naturally produces step-wise models.
Figure 3. Using isotonic regression to construct an empirical power market supply curve
Because physical grid operators dispatch generators in blocks of capacity from lowest to highest marginal cost, the true supply curve is literally a staircase. Isotonic regression allows our model to learn the physical supply stack empirically from market behavior, without us having to model individual power plant heat rates or fuel contracts.
5. The PJM Paradox: Solving the "Messy Blob"
In a market like ERCOT, mapping GSI to spark spreads with time buckets generally gets the job done beautifully. But when we applied this exact framework to Northeast markets like PJM, we ran into a wall.
When you plot Western Hub LMPs against local northeast gas prices using a standard 6.5 heat rate, the data doesn't look like a nice hockey stick. It looks like a messy, unreadable blob. There appeared to be no relationship between grid stress and spark spreads.
The breakthrough came when we realized that in the Northeast, extreme fuel price variations don't just shift the cost of power—they fundamentally alter market bidding behavior. To fix this, we introduced Fuel Price Regimes.
We segmented the PJM data by delivered gas price bands. When you filter the data through these regimes, the magic happens: the blob vanishes, and distinct, beautifully defined curves emerge.
Fuel Price Regime
Observed Market Behavior
Impact on Curve
Low Gas
Abundant cheap fuel; flat bidding behavior.
Flatter, lower inflection point.
Moderate Gas
Standard economic dispatch behavior.
Moderate, predictable "hockey stick."
High Gas
High opportunity costs; aggressive scarcity bidding.
Steeper, highly aggressive price acceleration.
When gas prices are high, generator bidding behavior becomes significantly more aggressive for the exact same level of physical grid stress. By building separate isotonic regressions for both different time buckets, outage regimes, and fuel price regimes, our model successfully masters the complexity of PJM and the Northeast.
Figure 4. Modified empirical market supply curves according to local delivered natural gas prices
6. Running the 1,000-Path Hourly Ensemble
With our mathematical curves established, we feed the model our probabilistic inputs: Sunairio ONE’s 1,000-path hourly GSI ensemble.
Because our weather-driven ensemble correctly forecasts thousands of fully correlated scenarios—perfectly capturing the joint probabilities of a heatwave, low wind speeds, and forced thermal outages—passing those GSI paths through our price regressions yields a true, mathematically rigorous probability distribution of hourly energy prices.
Conclusions
Empirical Supply Stacks: By utilizing isotonic regression, Sunairio's price model naturally replicates the step-wise nature of a physical dispatch stack without the data burden of traditional unit modeling.
Fuel Price Conditioning: In volatile markets like PJM, looking at grid stress alone isn't enough. Sunairio isolates distinct market behaviors by segmenting forecasts into specific fuel price regimes.
True Probabilistic Pricing: Power price risk lives in the tails. By pairing our fundamental price model with our 1,000-path weather ensemble, we provide users with a clear view of the frequency, duration, and severity of potential price spikes.
Now in the UI: Beginning now, users no longer need code to access these insights. The full power of our trading hub price forecast ensemble can be completely visualized and interactive right inside the Sunairio platform.
Sunairio Launches Asset-Level Generation Potential Forecasts to Fill Data Gap for Energy Traders & Grid Planners
By reconciling high-resolution weather ensembles with asset-specific Digital Twins, Sunairio provides the missing data needed to quantify curtailment, congestion, and basis risk across the grid, for thousands of utility-scale wind and solar farms throughout the US.
Baltimore, MD. — March 26, 2026 —Sunairio, pioneer of next-generation grid forecasting software, today announced the launch of its hourly Asset-Level Generation Potential Forecasts. This new capability provides hourly predictions for every utility-scale wind and solar farm across major power market regions, beginning with ERCOT.
Asset-level Generation Potential forecasts provide the high-fidelity visibility required to navigate the ever-evolving grid, helping to quantify the supply-side volatility that drives congestion, curtailment, and basis risk in modern power markets.
Sunairio generation potential forecasts differ from traditional regional wind and solar generation forecasts in two important ways:
They provide individual energy forecasts for every utility-scale project — not just regional aggregations — allowing users to anticipate local variability down to the nodal level.
They forecast potential generation compared to traditional renewable forecasts that are designed to forecast actual generation after curtailment.
By forecasting weather-driven potential generation, rather than post-curtailment energy, Sunairio Asset-Level Generation Potential Forecasts can be used as high-quality inputs for power flow and trading models. Traditional as-generated renewables data, on the other hand, can’t be easily used to model the grid because they represent a curtailed solution of the grid operator’s dispatch model, not an input to it.
"Trying to anticipate transmission congestion with traditional renewables forecasts is essentially guesswork to find the missing MWs," said Rob Cirincione, CEO, Sunairio. "Our Asset-Level Generation Potential Forecasts isolate the weather-driven energy potential from grid-enforced physical limits, allowing users to quantify the volume of energy at risk of curtailment before it happens."
The forecasts are powered by two proprietary Sunairio technologies:
Sunairio Digital Twins: High-fidelity, physics-based models of over 1,400 wind farms and 7,500 solar farms.
Sunairio ONE: A high-resolution weather forecast ensemble that provides the hyperlocal inputs necessary to drive the Digital Twins.
The launch of Asset-Level Generation Potential Forecasts provides critical, nodal-specific insights for a variety of stakeholders navigating today's increasingly complex power markets:
For nodal traders: By aligning generation potential with historical nodal price data, traders can predict the likelihood of nodal congestion.
For hub traders: Zonal generation potential aggregations reveal macro signals that drive zonal basis spreads. This signal is completely obscured in actual (post-curtailed) generation data.
For grid planners: The data serves as a high-precision input for production cost modeling, helping grid planners identify the exact transmission infrastructure needed to realize specific volumes of weather-driven energy potential.
Sunairio ONE represents a shift from reactive grid observation to predictive, physics-based intelligence. For more information, please visit sunairio.com or email info@sunairio.com.
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About Sunairio Founded in 2020, Sunairio is the pioneer of award-winning, next-generation grid forecasting software that’s the first to provide integrated energy, weather, and climate insights. Sunairio helps energy traders, grid operators, utility-scale asset developers, and VPP and demand response aggregators make better commercial decisions in the face of increasing grid variability and extreme event risks. Sunairio and Sunairio ONE have received recognition from the NSF, ACP, and EPRI. For more information, please visit sunairio.com.
Media Contact Logan Varsano, Inflection Point Agency for Sunairio logan@inflectionpointagency.com
The Pure Meteorological Signal: Launching Sunairio Asset-Level Generation Potential Forecasts
Generation Potential: The Pure Meteorological Signal
Forecasting the weather-driven energy production of wind and solar farms is critical for understanding the physical impact renewable generators have on the grid: variable power flow dynamics, regional supply-demand balances, and ultimately economic price risk. Yet the renewable generation data that’s typically been available to forecasters and grid modelers has a fundamental flaw: it’s reported after curtailment. That is, when historical renewable generation data is provided from public sources, those data sets report as-generated values that include losses due to curtailment (caused by transmission congestion that limits renewable output).
This means that forecasts designed to predict actual renewable generation are missing the most important piece of the story: the potential generation that could have been produced if not for the congestion – i.e., the true meteorological signal of renewable generation risk.
Today Sunairio is excited to fill this gap by launching hourly Asset-Level Generation Potential Forecasts for every utility-scale wind and solar farm across major ISO power markets, beginning with ERCOT. By combining high-resolution Sunairio ONE weather forecast ensembles with high-fidelity wind and solar farm Sunairio Digital Twins, Asset-level Generation Potential Forecasts provide the purest signal of variable generation-driven price volatility on the grid – from individual generator nodes all the way up to ISO-level aggregates and trading hubs.
Figure 1 provides a sneak peak of what this feature looks like in our platform.
Figure 1. (Left) Map of solar and wind assets in the Sunairio Maps module; (Right) Example wind asset forecast shown in Sunairio Ensemble Explorer module.
Why it Matters: The Unseen MWs that Actually Drive Congestion
The as-generated energy vs. generation potential nuance creates a vexxing problem for anyone trying to understand power market fundamentals. First, it means that we can’t try to replicate past market outcomes by simply using as-generated wind and solar values as inputs to engineering models of the grid – because that actual generation was potentially a curtailedsolution of the balancing authority’s dispatch model, not an input to it. Moreover, it means we can’t easily distinguish between A) an hour that had low renewable generation because the wind/solar resource was low and B) an hour that had low renewable generation because the wind/solar resource was so high that it was curtailed due to congestion.
Trying to triangulate the curtailment signal from LMP or raw weather is messy and difficult given the often non-linear relationship between weather and renewable generation. The fundamental signal we want is straightforward: weather-driven hourly renewable generation before curtailment.
How We Do It: Sunairio ONE + Sunairio Digital Twins
Creating Generation Potential starts with building a Sunairio Digital Twin – a high-fidelity physics-based model – of each asset. Sunairio Digital Twins incorporate plant-level technical characteristics (turbine coordinates, hub heights, power curves, PV tracking, PV panel efficiency, etc.) in addition to hyperlocal terrain modeling. For wind, this includes running a 100m-resolution CFD model over the farm footprint to translate mesoscale (3km) wind speed averages into 100m-resolution turbine-scale wind speed fields, and then factoring in turbine-by-turbine waking. For solar, we leverage the best-in-class PVLIB solar modeling suite to replicate asset-specific generation characteristics. These Sunairio Digital Twins already support our existing Historical Generation Potential dataset – covering more than 1,400 utility-scale wind farms and 7,500 utility-scale solar farms – and leverage our market-leading Sunairio High-Resolution Earth Data (SHED) as the source for historical weather.
With the development of Sunairio ONE, we’re now able to provide Generation Potential forecasts, in addition to historical Generation Potential, for all of these renewable sites (Figure 2). By isolating the weather-driven potential from the grid-enforced transmission limits, we enable our users to quantify the volume of energy at risk of curtailment before it happens – and identify high-conviction nodal arbitrage opportunities that post-curtailment forecasts overlook.
Figure 2. Process of creating Generation Potential given weather inputs and Sunairio Digital Twin model of a wind or solar asset.
Performance: Accuracy Where It Matters
We validated our Asset-Level Generation Potential Forecasts against data from IPP partners to confirm how they perform in real-world conditions. For this analysis, we limited our scope to sites that could provide a complete generation picture: hourly availability, hourly curtailment, and hourly net generation. By accounting for availability and adding back curtailment MW we’re able to validate our generation potential forecasts on an apples-to-apples basis.
On a portfolio of three solar sites and six wind sites, Sunairio’s forecast of Generation Potential using same-day and day-ahead weather forecasts achieved scores similar to that of using historical actual weather (Figure 3).
Figure 3. Forecast validation results for a solar portfolio and wind portfolio over three months.
For Nodal Traders: The "Electric Peninsula" Case Study
An analysis of the coastal wind farm cluster near the Ajo substation in ERCOT South demonstrates the impact of renewable generation on transmission congestion and highlights the value of Sunairio Generation Potential forecasts for nodal trading.
As shown in Figure 5, five wind farms totaling more than 1,000 MW compete for transmission capacity on the lone path off the coast: the Nelson Sharpe - Rio Hondo line. The Nelson Sharpe - Rio HondoGeneric Transmission Constraint (or NELRIO GTC) in Kennedy County, TX is a notorious bottleneck that ranked as the third most frequently binding constraint in 2025 (Potomac Economics) (Figure 5).
Figure 5. Map of five wind farms and the transmission lines associated with the Nelson Sharp- Rio Hondo Generic Transmission Constraint in ERCOT.
Using Sunairio’s Historical Generation Potential and aligning with three years of historical node and hub LMP data, we can explore the relationship between Generation Potential for this cluster of wind farms and the average node to South hub basis (Figure 6, left). (Note that from 2022 to 2025, the four nodes associated with this cluster of wind farms experience the same real-time LMP in 99.99% of hours.) As the left panel shows, as Generation Potential for that wind farm cluster increases, there is a non-linear shift in the occurrence of more negative local basis.
Traders can visualize this conditional probability of congestion as a surface with Generation Potential (Figure 6, right). Given a Generation Potential of ~900 MW, there is a 90% chance of the LMP basis being less than or equal to -$5, and a 85% chance of it being less than or equal to -$10. For virtuals traders trading into the day ahead market to take on congestion exposure, Sunairio asset-level Generation Potential forecasts are a powerful tool for building optimal bid/offer ladders and accurately quantifying the risk of the resulting trading strategy.
Figure 6. (Left) Sunairio Generation Potential vs. average node basis to South Hub of the five wind farms (2022 to 2025).(Right) Conditional probability of real-time congestion of the nodes associated with the five wind farms.
For Hub Traders: Generation Potential Zonal Aggregations Reveal Macro Signals
Asset-level generation potential forecasts aren’t just useful for nodal trading. When aggregated up to load zone or ISO-aggregates, they provide a powerful signal of the regional and market-wide curtailment that’s necessary to balance the grid. For example, Figure 7 shows a strong correlation between total ERCOT West Zone Generation Potential and actual West Zone curtailment, which isn’t visible when looking at Actual (post-curtailed) Generation. Predicting this dynamic via generation potential forecasts is critical for managing zonal basis risk.
Figure 7. Total ERCOT West Sunairio Generation Potential provides a strong signal of actual total renewable curtailment in the region while actual (post-curtailed) generation completely obscured the relationship
For Grid Planners: Precision for Planning and Production Cost Modeling
Beyond trading, this granularity is the new baseline for production cost modeling (PCM) and Grid Planning. Our hourly Asset-Level Generation Potential is ideally suited as an input for every individual renewable generation node of SCED models.
Further, Sunairio’s Generation Potential data enable planners to precisely quantify the incremental export capacity required to mitigate regional congestion and validate the specific transmission infrastructure necessary to support it. This shift moves the analytical focus from generalized capacity expansions toward targeted infrastructure investments designed to realize a specific volume of weather-driven energy potential.
See the Signal, Find the Missing MWs
The increasing penetration of intermittent renewables and the rising occurrence of unprecedented weather have rendered historical grid performance a diminishingly reliable proxy for future risk. The integration of Sunairio ONE-powered Generation Potential forecasts at the asset level provides the high-fidelity visibility required to navigate a fundamentally decentralized grid.
By reconciling high-resolution weather modeling – including terrain-induced fluid dynamics and advanced irradiance physics – with Digital Twin asset specifications, Sunairio replaces regional approximations with nodal-specific signals. This granularity is essential for quantifying the supply-side volatility that drives congestion, curtailment, and basis risk in modern power markets.Sunairio ONE represents a shift from reactive grid observation to predictive, physics-based intelligence. Want to see the Generation Potential for your most congested nodes? Contact us to run your portfolio through Sunairio ONE.
ERCOT’s 85 GW load forecast for Winter Storm Fern wasn't just high—it was indefensible
We’re one month removed from the havoc that was Winter Storm Fern. Focusing on its effects in ERCOT, we dug into the weather, grid, and market fundamentals to provide six strategic insights:
1. Five days out, temperature forecasts disagreed about timing and intensity but were reasonably correct about the event low/peak heating period on 1/26. While the peak heating event was relatively extreme, in this particular case it wasn’t much of a short-term surprise. In terms of long-run climatology though, the peak heating event on 1/26 was a P99 event for that calendar day, the week was a P97 and the month was P65.
2. ERCOT appears to have deliberately biased their load forecasts high (incorrectly). Assuming ERCOT was using weather forecast consensus (which was relatively accurate) as the input, we find their 85 GW forecast for 1/26 and 80 GW forecast for 1/27 to be essentially indefensible, falling well outside the range of recent weather-load relationships.
3. ERCOT appears to have deliberately biased wind generation forecasts low (correctly). We know this because their STWPP forecast (representing a P50 level) was below their WGRPP forecast (representing a P20 level). They appear to have manually adjusted the STWPP low but left the WGRPP alone, creating a mathematically impossible scenario.
4. Load in West Texas was significantly affected by icing of oil and gas infrastructure. A large share of load growth in West Texas has been driven by the electrification of oil and gas drilling and processing. When these wells/pipes froze, the associated electrical compression didn’t operate, resulting in exceptionally low load.
5. The market result: a high day-ahead (DA) clear driven by ERCOT’s posturing and then a real-time (RT) fail due to reality and load destruction. ERCOT’s posturing appeared to have the intended effect, supporting a high DA clear with ample reserves to blunt the risk of RT market capacity shortfalls — even in the face of lower than expected wind.
6. Sunairio’s price forecast explained the actual realization and the advanced market fear premium. Looking at our forecast of the 1/26 5x16 North Hub locational marginal pricing (LMP) realizations from the week before, we can see that our expected value was almost spot on, while the long tail to the right explains why some were willing to pay $600+ (because there was a small chance of clearing over $1,000). Note: the mean of our price distribution was at the 80th percentile — far from the median — because of the extreme right skew (risk of high prices).
Sunairio ONE In-depth Part 3: Beyond the Patchwork: Achieving Seamless 15-Year Hourly Ensemble Forecasting
Over the past few weeks, we have explored what makes Sunairio ONE a "next-generation" forecast. In Part 1, we discussed the necessity of a calibrated ensemble that accurately captures extremes. In Part 2, we demonstrated why high spatial and temporal resolution is critical for modeling modern renewable assets like wind and solar. In this final installment, we show that Sunairio ONE provides a unified, seamless outlook from hours to years, eliminating the fragmentation issues that the industry faces today.
The Current Landscape: A Patchwork of Compromises
Today, energy traders, grid operators, and other energy professionals have access to a growing collection of public weather forecasts that are each published with differing outlook horizons, temporal resolutions, and refresh schedules.For example, NOAA’s HRRR provides hourly forecasts for the next two days. Other forecasts, such as the GFS or ECMWF’s IFS, stretch to about 2 weeks, but with lower temporal resolution further in the outlook period (see Figure 1, top panel). Seasonal-range forecasts such as the CFS or IFS SEAS look many months into the future, but are low temporal resolution (see Figure 1, bottom panel) and in the case of the SEAS, published only once per month, letting forecasts go stale quickly. While most weather forecasts are updated just four times per day (e.g., 00Z, 06Z, 12Z, and 18Z) or fewer, Sunairio ONE is refreshed each hour using the latest information.
To look beyond 9 months, one must turn to climate models (the current iteration of models are known as CMIP6) instead of weather forecasts, which can be significantly biased, typically provide only vague daily averages, and obscure intraday volatility.
Figure 1. (Top panel) Even within a short 16-day outlook, alternative forecasts provide sparse data with low temporal resolution across days while Sunairio ONE provides dense hourly data without gaps; (Bottom panel) For seasonal or longer outlook periods, only Sunairio ONE provides hourly data.
Thus, in order to build a complete picture of future weather risk– both in the short-term and in long-term planning– energy traders and asset managersneed to stitch together information across multiple sources in a patchwork manner as illustrated in Figure 2. This fragmented approach requires building multiple complex data pipelines, and perhaps more importantly, it makes it challenging to synthesize insights for key operational and planning decisions. Sunairio ONE provides the seamless solution that the industry needs.
Figure 2. The standard industry approach involves stitching together different models with varying resolutions, creating data "seams" and leaving a massive void for long-term planning. Sunairio ONE provides a seamless long-term outlook.
The Sunairio ONE Advantage: The Unbroken Line
Sunairio ONE was designed to eliminate the seams that exist when moving across timescales, providing full continuity and high resolution.
How is it possible to generate a credible hourly ensemble forecast a decade in advance?
It requires bridging the gap between traditional numerical weather prediction (NWP) and long-term climate modeling. Sunairio ONE is not just "extending" a standard weather model until it falls apart. It utilizes a proprietary blend of physics-based modeling and AI-driven calibration.
As detailed in our overview of ENSO-informed climate simulations, our long-range ensembles are constrained by large-scale climate signals, such as El Niño and La Niña cycles. This ensures that the weather patterns generated in years 5, 10, or 15 are physically consistent with the broader climate realities expected during those periods, while still providing the hourly volatility required for asset modeling.
The Business Impact: Strategic Consistency
Moving from a fragmented patchwork to a seamless solution offers more than just technical convenience; it solves fundamental business problems:
Eliminating "Model Basis Risk": When a short-term trading desk uses one model and the term traders use another, the firm risks making operational decisions that contradict a strategic outlook. A unified model ensures everyone is reading from the same book.
Aligning Analytics with Market Contracts: Power markets operate on financial timelines (e.g., balance-of-month (balmo), seasonal (summer/winter), and annual contracts) that frequently collide with the rigid boundaries of standard weather forecasts. Sunairio ONE solves this misalignment by providing an unbroken, 15-year hourly stream.
Streamlined Data Pipelines: Data engineers no longer need to build complex intake systems to parse GRIB files from four different government agencies, normalize the data, and try to stitch it together. Sunairio ONE offers a single API feed and consistent timeseries format.
Accurate Long-Term Valuation: To value an energy asset over its lifecycle, modeling assessments need realistic forward-looking weather assumptions that replicate trends and volatility instead of simple historical averages. Sunairio ONE’s 15-year hourly ensemble captures trends, replicates extremes, and provides the necessary fidelity for accurate long-term financial modeling.
The Future of the Grid is Unified
Over this three-part blog series, we have outlined why the energy transition demands a new class of forecast technology. The grid of the future cannot run on forecasts that fail to see extremes, lack necessary resolution, or fragment after two weeks.
Sunairio ONE delivers unprecedented fidelity at all time scales. It is calibrated, sharp, high-resolution, and, crucially, seamless. It’s time to stop stitching together forecast models and start solving energy challenges with a unified view of the future.To see the difference seamless data can make for your organization, contact us today for a demonstration of the Sunairio ONE 15-year hourly ensemble.
Sunairio ONE In-depth Part 2: Resolution Matters
This is the second blog post in our three-part series that explores key areas where Sunairio’s Omniscale Next-generation Ensemble (ONE) forecast model outperforms traditional solutions. Our first blog post demonstrated that Sunairio ONE was more calibrated, sharp, and extremes-conspicuous than legacy ensemble methods. Here, we show that Sunairio ONE’s high spatial resolution captures local variability of wind speeds better than existing models and demonstrate how that wind speed gradient can translate to a large variation in expected power output.
Introduction
Variable renewable energy assets like utility-scale wind and solar farms experience meaningful fluctuations in power output as local weather conditions change. However, most weather forecasts aren’t generated at a spatial or temporal granularity that’s sufficient to accurately anticipate those dynamics. In the case of wind, for example, detailed topographical features and their resulting phenomena (e.g., terrain-induced waking or speed-up effects) are lost at coarse resolutions1. Sunairio ONE addresses these challenges by providing a high resolution weather forecast, which captures small-scale variability, enabling best-in-class asset-level generation potential forecasts.
Wind farm forecasting best practices
Asset-level wind energy forecasting requires both highly-accurate weather forecasts of mesoscale wind (on the order of a few km) and sophisticated models of wind field dynamics within the wind farm footprint, which are affected by many factors including terrain, vegetation cover, and turbine waking.
In fact, we find that wind energy modeling best practices dictate separating this problem into three stages: 1) the mesoscale free-field wind2 forecast, 2) the microscale wind field within the wind farm footprint (less than 1km effects usually influenced by terrain), and 3) turbine-by-turbine waking (Figure 1).
Figure 1. Diagram showing three mains steps involved in accurate modeling wind energy at a wind farm.
We are now excited to leverage Sunairio ONE, our high-resolution ensemble weather forecast, to improve mesoscale wind forecasting for asset-level generation.
Existing weather forecasts are too coarse, or fine-but with limited outlook
Surveying the landscape of publicly available weather models that can provide mesoscale free-field wind forecasts shows that none offer truly high spatial and temporal resolution output that extend beyond a few hours (Table 1). Furthermore, Figure 2 helps provide some scale to the current problem, comparing the spatial resolution of Sunairio ONE (at approximately 2km) and the IFS (at approximately 10km) against actual wind farm footprints in ERCOT. Mesoscale forecasting at 10km (like the IFS) vs. 2km may average out important wind gradients that vary over a wind farm and lead to exponential wind generation errors (more on this below).
Additionally, Sunairio ONE forecasts are generated at hourly temporal resolution–critical for energy forecasting applications–for the full forecast period; no other major publicly available forecast model does this. For example, the IFS starts at hourly resolution for the first three to four days but then drops to 3-hourly and 6-hourly steps as it gets further out.
0-90 at 1-hourly 93-144 at 3-hourly 150-360 at 6-hourly
0.1°
~10km
AIFS/AIFS ENS (ECMWF)
15 days
0-360 at 6-hourly
0.25°
~28km
Sunairio ONE
15 years
1-hourly
0.02°
~2km
Table 1. Comparison of forecast models by outlook period, temporal resolution, and spatial resolution.
Figure 2. Zoomed area of map of wind farms in Texas (shaded bounding boxes show footprints of wind turbines at wind farms).
Wind is highly variable across wind farm scales
To visualize just how important it is to capture variations in free-field wind forecasts, we analyzed a day of wind forecast performance (three days out) between Sunairio ONE and the IFS across a major ERCOT wind farm, Capricorn Ridge. As Figure 3 shows, the difference between wind speeds at the north end of the farm compared to the south end (turbines marked in red in left panel) were as great as 3 m/s in some hours. While Sunairio ONE successfully forecasted this wind field gradient (right panel), the lower-resolution IFS could not resolve the proper dynamics, instead seeing a fairly uniform wind field.
Figure 3. (Left panel): Map of Sunairio ONE forecasted wind speed across a wind farm in Texas over a 24-hour period; (Right panel): Line plot tracking the wind speed gradient (i.e., difference in wind speeds) from the north side to the south side of the farm compared toSunairio ONE 3-day ahead forecast and IFS ENS 3-day ahead forecast.
Why it all matters: Even seemingly minor weather variations can have outsized impacts in power output
Does a 3 m/s gradient in free-field wind speed over a wind farm really matter? Depending on the absolute wind speed levels, it can matter immensely. Below their rated capacity, wind turbine power output scales cubically with wind speed which causes even seemingly minor variations in wind speed to lead to large differences in generation potential. Turning to Figure 4, we plot an example power curve of a 1.5 MW turbine, which represents the majority of turbines at Capricorn Ridge. A difference of 3 m/s in wind speed can translate to up to a 959 kW delta in power output, or 64% of rated capacity!
Figure 4. Power curve for the 1.5 MW wind turbines that comprise the Capricorn Ridge Farm. 3 m/s wind speed gradients can translate to power differences as large as 959 kW.
Over a wind farm with hundreds of turbines, the impact of modeling wind speeds at a lower resolution on generation potential forecast performance can quickly add up.
Conclusion
Accurately forecasting local wind speeds is essential for creating reliable forecasts of asset-level generation. Sunairio ONE provides 2km spatial resolution, hourly temporal resolution forecasts that are ideally suited to serve as the critical free-field wind forecast step in wind farm generation modeling pipelines.
Free-field wind speeds are wind speeds expected in open, unobstructed areas free of turbulence caused by structures such as wind turbines. ↩︎
The HRRR goes out to 48 hours only for the 0Z, 6Z, 12Z, and 18Z initializations. All other initializations go out 18 hours. ↩︎
Sunairio ONE In-depth Part 1: Making a Calibrated and Extremes-Conspicuous Ensemble where Benchmark Models Fail
This is the first edition in a series of three blog posts that explores the commercial shortcomings of legacy forecast solutions compared to the advantages of Sunairio ONE.
Introduction: Why the Power Sector Needs Ensemble Forecasts
While every business relies on forecasts to some extent, power sector businesses are forecast super-consumers, especially of weather and weather-based forecasts for power demand, wind generation, and solar generation. In fact the growth of renewables has strengthened the link between weather and energy, introducing weather variability to the supply side of the power grid supply-demand balance. This exponentially increases both the requisite forecast complexity of everyday grid operations and the manifest importance of reliable forecasts themselves.
Regardless of their specific role, power-sector forecast consumers fundamentally make quantitative business decisions that rely on weather forecasts. Yet this business need runs straight into two of the most confounding aspects of weather and weather forecasts: the inherent volatility of weather itself and the large uncertainty of weather forecasts.
Most sophisticated weather forecast users have long recognized that the best-practice solution to this problem is to not rely on one forecast, but many. An ensemble approach to forecasting involves issuing many alternative forecasts for the same phenomenon. A common use case is the ubiquitous hurricane track forecast plot that creates an ensemble of many possible trajectories to convey a sense of probabilistic forecast tracks. There are many ways to generate a forecast ensemble and the concept isn’t limited to weather forecasting, but it is widely accepted to be one of the best methods for investigating forecast uncertainty.
Creating an ensemble weather forecast theoretically satisfies two important needs for decision-makers: 1) it theoretically describes the range of possible outcomes, and 2) it can be used to calculate the implied probabilities of all outcomes within the range.
Properly seeing the full range of outcomes – including extreme events – is absolutely critical in power grid applications because extreme events have an outsized effect on reliability and economic outcomes. For example, in the ERCOT real-time power market, just 1% of hours per year account for one-third of total annual market value1. This means that having a clear view of tail risks is paramount for any power grid planner or power market participant, lest they make decisions with a limited forecast set that hides massive risk.
In this blog post, we evaluate how successful traditional ensemble forecasts and the new Sunairio ONE ensemble are at generating reliable and actionable predictions for power grid/power market applications. We evaluate these ensemble forecasts in three dimensions: calibration (having correct probabilities), sharpness (seeing events clearly), and a new ensemble forecast performance dimension we introduce here: the principle of conspicuous extremes (seeing extreme or tail events).
Classes of Ensemble Weather Forecasts
Ensemble weather forecasts come in many flavors, from the simple historical analogue (a set of historical observations, like historical weather years) to compute-intensive traditional numerical weather prediction (NWP) algorithms, to highly complex deep learning-based generative AI models.
Historical Weather Analogue Ensembles
An historical analogue ensemble of, say, 30 years, constructs a thirty member ensemble where each member of the ensemble is a year of observed weather in the last 30 years. As Historical weather analogues rely on weather observations, serially-complete hourly analogues are generally limited to 75 years or less.
Numerical Weather Prediction Ensembles
Starting from real atmospheric observations, NWPs simulate physical dynamics of the atmosphere to generate a model of future weather. Due to the complex and chaotic nature of atmospheric fluid dynamics, small perturbations in initial weather conditions can lead to dramatically different weather paths. NWP ensembles can take advantage of this by perturbing the initial conditions to simulate an ensemble of “representative” weather realizations. The IFS Ensemble (IFS ENS), an NWP forecast produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), is the gold standard against which all modern weather forecasting systems are compared due to its relatively sharp, skillful short-term forecasts. The IFS ENS consists of one control forecast and 50 additional ensemble paths.
Generative AI Weather Forecast Ensembles
Advances in deep learning have spurred a new paradigm of weather forecasting that borrows ideas from generative AI to produce global weather forecasts that are (1) competitive against the IFS Ensemble and (2) far more computationally efficient. Even without knowing any physical laws, these models, trained on historical reanalysis weather data, produce physically realistic weather patterns. The ECMWF has released an operational ensemble AI weather forecast, the AIFS Ensemble.
Ensemble Performance Metrics: Calibration, Sharpness, and the Ability to See Extremes
The taxonomy of ensemble forecast methods is broad, but many suffer from the same problems: poor calibration, lack of sharpness and poor visibility of extreme events. All of these dimensions are critical for a power sector forecast consumer. A sharp but uncalibrated ensemble will be overly confident but often wrong. A skillful calibrated ensemble that doesn’t satisfy the principle of conspicuous extremes will leave a forecast user exposed to lurking tail risks; you can’t plan for what you can’t see.
Figure 1 illustrates these dynamics. Starting from the left, panel A shows a hypothetical ensemble forecast that is calibrated but not sharp (its implied probabilities are correct but its poor skill means that the ensemble range is very wide), panel B shows an example of an ensemble that is sharp but uncalibrated, while panel C presents an ensemble that is both sharp and calibrated but completely missed an event outright because it does not model extremes. The ensembles in the first two panes have very obvious deficiencies while the ensemble in the third pane exposes the forecast user to more insidious risks – the forecast looks reasonable, performs well in normal weather conditions, but quietly hides tail risks.
Panel D, on the other hand, depicts an ideal ensemble forecast: sharp, calibrated, and extremes-conspicuous.
Figure 1. In the first three panes we show three hypothetical 50 path forecast ensembles that each suffer in one of the three forecast performance dimensions: calibration, sharpness and the principle of conspicuous extremes. In the third, we show an ideal ensemble with 10x as many paths. It is just as sharp as panel C but with more paths, capturing the tails of the distribution.
As we show in this blog post, legacy ensemble forecasts commonly exhibit the deficiencies shown above in A-C, while the new Sunairio ONE method is intelligently designed to replicate the ideal ensemble paradigm in D.
A New Paradigm for Ensemble Forecasting: Sunairio ONE
Created in response to the significant limitations of the traditional ensemble forecast classes, Sunairio ONE is powered by a hybrid forecast architecture and designed to maximize commercial actionability. The ONE forecast begins by creating a 1,000-path weather ensemble via a long-range generative weather algorithm that samples from known distributions. Within the medium range window (1-14 days), a deterministic Control forecast is then created by solving an optimal forecast combination problem given the most recent public model guidance. Finally, the original seasonal ensemble is reshaped – conditional on the Control forecast – such that the expected ensemble forecast error matches the ensemble spread.
In the following sections we compare the ensemble forecast of Sunairio ONE to benchmark models in applications for both short and long range forecasting.
Empirical Ensemble Model Performance Analysis
Long-range Forecasts
Over longer time horizons (months to years), use of the Historical Analogue approach for ensemble forecasting in the power sector is commonplace as there are no skillful hourly weather forecasts. Accordingly, we compare how well Sunairio ONE performs over a long lead time by examining its performance against a 74-year Historical Analogue. As Figure 2 shows, we plot the ensemble calibration of Sunairio ONE against the Historical Analogue for a 12-month forecast that was issued with 6-18 months forecast lead time, using Daily Maximum Temperatures at Philadelphia airport (PHL). This type of plot, known as a probability-probability (PP) plot, compares the implied ensemble event probability (x-axis) to the actual observed event frequency (y-axis). As the plot shows, a 74-year historical ensemble is poorly calibrated (note how it generally underpredicts the probability of daily maximum temperatures– is biased low–a result of not adjusting for climate change), while the Sunairio ONE ensemble closely hews to the 1:1 line that represents good calibration: implied ensemble forecast probabilities = observed frequencies.
Figure 2. PP plot of 74-year Historical Analogue ensemble and Sunairio ONE for daily maximum temperature for the period July 2024 to June 2025. Historical Analogue for the years 1950-2023. Sunairio ONE trained through 2023 and then predicted for the Jul-24 to Jun-25 period (6 to 18 months forecast lead time)
The Historical Analogue approach also performs poorly when considering its ability to see extremes. As Table 1 shows, the 74-year Historical Analogue completely missed 184 hours per year within the analogue range (a forecast surprise), as there are simply too few samples (74) to accurately represent the range of likely weather over an 8,760 hourly period. By contrast, Sunairio ONE captures 97% of those missed extreme values (178 of the 184 misses) due to the more robust 1,000-member ensemble that’s capable of seeing well past 99th percentile events.
# of Hourly 2m Temperature Surprises, PHL
July 2024 - June 2025
74 Year Historical Analogue (1950-2023)
184
Sunairo ONE (6-18 months out)
6
Table 1. Hourly 2m temperature surprises at PHL for the period July 2024 to June 2025 from two different approaches to long-range ensemble forecasting
Short- and Medium-range Forecasts
Over shorter time horizons we compare Sunairio ONE to benchmark medium-range (14-day) public ensemble forecast models. As Figure 3 shows, Sunairio ONE again outperforms traditional benchmarks such as the ECMWF’s IFS Ensemble by generating a calibrated forecast – even through the upper quartile event range (events in the 75th to 100th percentile). As we see, Sunairio ONE replicates extreme events at the correct observed frequencies while the IFS Ensemble (the gold standard NWP ensemble) drastically underpredicts them.
For example, the dotted green line indicates that temperatures predicted by the IFS Ensemble to occur no more than 5% of the time (the 0.95 non-exceedance probability level) actually occurred more than 21% of the time (a non-exceedance frequency of about 0.79), meaning that the these temperature events were about 4 times more likely than the IFS ensemble implied.
Figure 3. PP plot of Sunairio ONE and the IFS Ensemble for hourly temperature at PHL. PP plot is zoomed in to the upper quartile. Dotted green line shows that the IFS Ensemble p95 level is actually the observed p79 frequency.
Moreover, Sunairio ONE isn’t just calibrated – it’s also demonstrably sharper than traditional ensemble forecasts. Comparing the continuous ranked probability score (CRPS – a metric used to evaluate the accuracy of a probabilistic forecast) of Sunairio ONE to the GFS Ensemble (known as GEFS, NOAA’s benchmark medium-range forecast ensemble) we see that Sunairio ONE improves CRPS across a suite of weather variables by 20% or more (Figure 4).
Figure 4. Relative improvement in CRPS for Sunairio ONE compared to GEFS across several weather variables and daily aggregations.
The Sunairio ONE ensemble also does a much better job at seeing extreme events than traditional forecasts. Here we note that traditional weather forecasts actually tend to be worse at seeing extremes the closer they are to happening. For example, Figure 5 plots the number of hourly temperature forecast surprises (events in which the actual temperature realized completely outside of the ensemble range) for the IFS Ensemble at PHL over a period from June 20 to September 22, 2025 – with just one day of lead time. That is, we collected IFS Ensemble temperature forecasts for the following calendar day every day during this period and counted the number of hours in which hourly temperatures realized above or below the 51-member ensemble set.
Over this 95-day period, there were a total of 412 hourly forecast surprises (including misses above and below the ensemble range). Notably, the IFS Ensemble was nearly perfectly unbiased during this period at PHL, with MBE measuring +0.02 deg F, meaning the misses were not caused by the ensemble range being systematically shifted up or down. Rather, the forecast was simply overconfident (and wrong). We do not show the Sunairio ONE forecast surprises in this plot because the Sunairio ONE ensemble did not miss any hours.
Figure 5. Daily counts of hourly forecast surprises (hourly realizations outside the 51-member ensemble range) for the IFS Ensemble, June 20 to September 22, 2025 at PHL, with one day of forecast lead time.
Next we measure the forecast surprises on daily maximums and daily minimums for 1 day, 7 days, and 14 days forecast lead time and compare them to the number of forecast surprises that should be expected given the ensemble size. Table 2 presents the results. As the table shows, the IFS Ensemble missed 3 daily max/min events at 14 days and 8 daily max/min events at 7 days (compared to an expected range of 2 to 6), but missed a whopping 17 days (18.5% of the analyzed period) at just 1 day of forecast lead time. This data suggests that the IFS Ensemble has an inherent underdispersion weakness which becomes more profound as the forecast lead time decreases.
To estimate the magnitude of the underdispersion, we calculate how much wider the ensemble would have to be so that the actual misses fall within the expected range. As the table shows, the underdispersion is at least 1 deg F at 7 days of lead time and more than 3 degrees at 1 day of lead time.
Lead
Sunairio ONE exp. # surprises
Sunairio ONE # surprises
IFS ENS exp. # surprises
IFS ENS # Surprises
IFS ENS Underdispersion
1 day
0 to 1
0 (0%)
2 to 6
17 (18.5%)
> 3F
7 days
0 to 1
0 (0%)
2 to 6
8 (8.6%)
> 1F
14 days
0 to 1
0 (0%)
2 to 6
3 (3.3%)
0F
Table 2. Expected forecast surprises, actual forecast surprises, and estimated underdispersion for Sunairio ONE and the IFS Ensemble. 2m temperature daily maximum and daily minimum forecasts at PHL, one day forecast lead time, June 16 to September 28, 2025.
Real-world Implications of Bad Ensemble Forecasts
Forecast surprises such as these have immense consequences for power grid operators and power market participants because they represent hidden risks that generally haven’t been accounted for in any decision-making. That is, traditional ensemble weather forecasts, especially those issued for the next calendar day, are commonly thought to encapsulate a complete range of risk–but they do not.
In fact as the left column of Figure 6 shows, during a three-day period from September 26 to 28 this year the IFS Ensemble range missed each day’s maximum temperature just one day out. There are 51 ensemble members within the IFS ENS and not a single one was high enough.
Comparing that performance to Sunairio ONE (right column of Figure 6), we see that while the realized temperature was indeed towards the upper range of our one-day forecast, it was well within the ensemble spread–visible to any forecast user making quantitative scheduling, dispatching, or trading decisions.
Figure 6. One day lead time 2m temperature forecast ensemble range for IFS Ensemble (left) and Sunairio ONE (right), along with observations. PHL airport, September 26-28, 2025.
As these plots show, Sunairio ONE ensembles exhibit the overall characteristics of the ideal ensemble forecast from Figure 1: calibrated, sharp, and showing the risk of extremes.
To put the IFS Ensemble forecast surprises in context of power grid balances, we convert the temperature misses seen here into hidden PJM load risks (assuming a similar temperature surprise across the PJM footprint). Figure 7 plots the result, showing that a ~2-4 deg F temperature miss across these hours accounts for up to 6 GW of hidden load risk–equivalent to the regional power demand of the entire Philadelphia metro area!
Figure 7. Magnitude of potential hidden PJM load due to IFS Ensemble temperature surprises, September 26-28, 2025, and daily max load for PECO (Philadelphia Electric Company).
This means that a major city’s worth of demand risk was hiding in plain sight, invisible to forecast users because the benchmark models fail to see extremes – even just one day out.
Conclusion
Ensemble forecasts are critical tools for grid operators, power market participants, and other decision-makers in the energy sector. However, legacy ensemble forecast methods suffer from chronic and significant deficiencies that obscure important risks: missing hundreds of hours per year, misrepresenting event probabilities by a factor of 4, and hiding extreme events equivalent to a city’s-worth of power demand.
Sunairio ONE addresses these problems by intelligently generating an ensemble forecast that’s calibrated, sharp, and sees the all-important extreme events.
Sunairio unveils ONE, a next-gen grid forecast model with unprecedented fidelity at all time scales
Sunairio ONE captures 97% of extreme events that traditional forecasts miss, providing critical intelligence for grid reliability, power systems planning, and energy markets
Baltimore, MD. — October 16, 2025 —Sunairio, pioneer of next-generation grid forecasting software that provides critical weather and energy insights, announced today that it has launched Sunairio ONE (an Omniscale Next-generation Ensemble). Sunairio ONE now powers the company’s award-winning software for modern energy risk management.
Anticipating granular grid risk today is a daunting task that’s frustrated by a disjointed and inadequate technology landscape. Conventional weather forecasts are complex and slow, limiting their usefulness to modeling near-term events over relatively large areas — with forecast scenarios that misrepresent the chance of extremes. Longer-term forecasts are so inaccurate that the meteorological agencies themselves warn against using the data directly, forcing the industry to rely on historical analogues instead. And the new AI weather forecasts are unfortunately linked to these problems because they train off the same old data.
Sunairio ONE makes these approaches obsolete.
“We built Sunairio ONE from the ground up to solve these challenges, enabling unparalleled resolution, accuracy, and correct probabilities, complete with seamless coverage for the next hour, the next 15 years — and anywhere in between,” said Rob Cirincione, CEO, Sunairio.. “The key is our unique data — Sunairio ONE is trained on the Sunairio High-resolution Earth Dataset (the SHED), our proprietary, high-resolution historical weather data archive that’s purpose-built to replicate granular risks for the modern grid.”
Sunairio started with the industry’s benchmark historical weather data, leveraged machine learning to sharpen resolution by 100x, then trained ONE on that data while incorporating changing climate fundamentals. Sunairio ONE generates 1,000-member hourly ensembles compared to traditional methods that stop at 50. The result is unprecedented forecast fidelity for load, renewables, and grid stress across all time scales.
“Alternative forecast methods rarely issue more than 50 forecast scenarios, preventing users from seeing the make-or-break events,” Cirincione explained. “Imagine if I told you there was a 1-in-50 chance of winning the lottery, except you don’t win the lottery, you black out the power grid or bankrupt your company. That’s the risk we’re taking with these limited forecasts.”
“More recently, there’s been a burst of new AI-powered weather forecasts, but what often gets overlooked is that these AI models are only as good as the data they’re trained on — and these models have all been trained on the same public, relatively low-resolution archive,” continued Cirincione. “That means they’re all susceptible to missing the same events — especially extreme events — which are critical in modern power grids where just 1% of hours account for 30% of grid stress.”
A larger forecast ensemble also improves accuracy. For example, compared to grid-level forecasts provided by ISO operator ERCOT, Sunairio’s hourly wind and solar forecasts are up to 20% more accurate on average while capturing the true risk of forecast misses and unprecedented extremes in the ensemble distribution.
Sunairio ONE is an evolution of the company’s award-winning, long-term generative forecast technology — previously recognized by the National Science Foundation, American Clean Power Association, and EPRI. The company’s high-resolution, large-ensemble approach to forecasting now spans both seasonal and climate scales (from 15 days to 15 years) as well as shorter-term 1- to 15-day forecasts.
"No other solution gives us weather and energy forecasts from a 1,000-member ensemble and continuously tracks changes to load growth and infrastructure buildout," said Josh Henson, Vice President, Wholesale Trading, Constellation. “We’ve been pleased with Sunairio as a platform to model probabilistic weather and energy risks for longer-term power markets. Sunairio ONE provides those same insights for short-term markets enabling better business decision-making that benefits our customers.”
Sunairio ONE forecasts support actionable intelligence for reliable planning across power markets and utility operations. It is designed to support a range of use cases, including:
Probabilistic energy trading and hedging strategy: Ensembles predict future energy price ranges and show potential for risk versus reward.
Asset-level renewables forecasting: Sunairio ONE provides probabilistic forecasting for individual renewable energy assets (including availability and curtailment losses) for independent power providers, utilities, and public market participants.
Climate-aware portfolio risk management: Accurately calculates the risk to energy investments resulting from weather-driven impacts across load, wind, and solar.
“Some of the greatest risks to power grids are hiding in plain sight. They don’t always look like hurricanes, which makes them more insidious. Increasingly, they look more like unusual combinations of weather that increase demand while reducing renewables output: heat that lingers past twilight or polar vortex cold snaps that arrive with calm winds,” said Raiden Hasegewa, PhD, Director of Data Science, Sunairio. “By combining weather and energy in a sophisticated forecast model, we’re giving companies insight they can’t get anywhere else.”
All of Sunairio’s energy and market models built on top of Sunairio ONE automatically update and retrain continuously, keeping pace with fast-moving fundamentals. For more information, please visit sunairio.com or email info@sunairio.com.
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About Sunairio Founded in 2020, Sunairio is the pioneer of award-winning, next-generation grid forecasting software that’s the first to provide integrated energy, weather, and climate insights. Sunairio helps energy traders, grid operators, utility-scale asset developers, and VPP and demand response aggregators make better commercial decisions in the face of increasing grid variability and extreme event risks. Sunairio and Sunairio ONE have received recognition from the NSF, ACP, and EPRI. For more information, please visit sunairio.com.
Media Contact Nikki Arnone, Inflection Point Agency for Sunairio nikki@inflectionpointagency.com
