--- title: "Renewable energy forecasting methodology" description: "Technical overview of Steadysun’s methodology. How our models combine satellite nowcasting, NWP models, and AI for solar and wind energy simulation." url: "https://steady-sun.com/renewable-energy-forecasting-methodology/" date_modified: "2026-09-21T11:07:43+02:00" --- # Solar & Wind energy forecasting methodology A complete guide to Steadysun’s renewable energy forecasting methodology, from satellite nowcasting to multi-model NWP, AI calibration, probabilistic outputs, and the full parameters catalogue. [Book a demo](https://steady-sun.com/contact-us/) [More about us](https://steady-sun.com/about-us/) --- ## From weather to watts ## Why forecasting solar and wind output starts with managing uncertainty **Solar power forecasting** is the process of predicting the electricity output of a photovoltaic over a defined time horizon, from the next minute to 15 days ahead. **Wind energy forecasting** applies the same core discipline to turbines, replacing irradiance physics with wind speed and turbulence dynamics. Both disciplines share a common problem: weather is uncertain, and that uncertainty costs money. The goal of any serious **renewable energy forecasting** system is to shrink that uncertainty as much as physically possible, then quantify what remains so operators can act on it. ![solar-panels-field-wind-turbines-horizon-renewable-green-energy](https://steady-sun.com/wp-content/uploads/2026/09/solar-panels-field-wind-turbines-horizon-renewable-green-energy.webp) ## How it works ### Forecasting methods explained Since 2013, Steadysun has been refining how it forecasts solar and wind power, **fusing live data from 5 geostationary satellites, 20+ numerical weather prediction (NWP) models, and real-time plant measurements**. This gives deterministic and probabilistic forecasts from 0 to 15 days ahead, updated as frequently as every 5 minutes, delivered worldwide via API or SFTP. There are three broad families of methods behind this fusion. In practice,** the most accurate systems use all three together**. ![Weather models over Mauritius and Réunion: ARPEGE, GFS, AROME, GDPS, ICON, IFS HRES](https://steady-sun.com/wp-content/uploads/2025/06/models-over-mauritius-and-reunion-island1.webp) NWP models **Numerical Weather Prediction (NWP) models** solve the equations of atmospheric dynamics on a 3D grid, starting from observed initial conditions and stepping forward in time.  Models like ECMWF’s IFS, NOAA’s GFS, Météo-France’s ARPEGE, or the regional WRF (which can reach 1 km resolution) are the backbone of any serious day-ahead or medium-range forecast. **For solar**, NWP outputs (cloud fraction, aerosol optical depth, humidity) feed into irradiance models. **For wind**, NWP wind fields are downscaled to hub height using boundary-layer physics or statistical transfer functions. The limitation: NWP models have systematic biases that vary by location, season, and weather regime. No single model is best everywhere, all the time . ### Forecast horizon D+1–D+15 (day-ahead & medium-range) ### Update frequency up to every hour ### NWP spatial resolution 10–50 km (global) 5–10 km (regional high res) 1–5 km (regional high res) Down to 1 km (regional WRF) ![Twelve animated satellite simulations for ESA project](https://steady-sun.com/wp-content/uploads/2025/08/european-space-agency-and-steadysun-project-satellite-imagery.gif) Advanced multi-satellite forecasting **For the next few minutes to a few hours, live satellite imagery outperforms NWP models**, which simply can’t update fast enough to capture a cloud passing over a plant right now. **The principle**: track cloud motion across multiple spectral channels (visible, near-infrared, thermal infrared) between successive images, then extrapolate cloud cover and irradiance forward in time. The limitation: this approach loses accuracy fast beyond a few hours, since cloud motion extrapolation can't anticipate new cloud formation or dissipation, that's exactly where NWP models take over. ### Forecast horizon 0–30 min (nowcasting) and 30 min–3 h (intraday) ### Satellite update cadence every 5–15 minutes ### Satellite spatial resolution 500 m–3 km ![IT server rack](https://steady-sun.com/wp-content/uploads/2025/03/taylor-vick-M5tzZtFCOfs-unsplash-scaled.jpg) Hosting and computation environment Statistical approaches, from simple persistence (assuming the next hour looks like the current hour) to [**ARIMA time series models**](https://www.ibm.com/think/topics/arima-model), work well at very short horizons where the atmosphere hasn’t changed much. **Machine learning goes further**. Neural networks, gradient boosting, and ensemble methods can learn non-linear relationships between NWP outputs and observed power, correcting biases that physics-based models miss. They need training data (typically at least one year of co-located production and weather records) but once trained, they adapt continuously as new data arrives. The limitation: purely data-driven models can struggle in rare or extreme weather situations that weren't well-represented in the training set. ### Hybrid multi-model approach : how Steadysun does it Steadysun’s approach is neither purely physical nor purely statistical. It’s a hybrid multi-model engine that combines the strengths of both. **The core idea:** run 20+ leading NWP models in parallel, then use AI to select, weight, and merge their outputs into a single, more reliable forecast. The AI assigns dynamic weights to each NWP model based on its recent performance over a rolling window. This is conceptually similar to what the forecasting community calls a “meta-forecast” or ensemble post-processing, the same principle behind [**ECMWF’s ensemble prediction system**](https://www.ecmwf.int/en/learning/training/introduction-chaos-predictability-and-ensemble-forecasts), but **applied operationally at the plant level and updated every 5 minutes.** The result is a** forecast that’s more robust** than any individual NWP model, because the errors of different models tend to be uncorrelated. **When one model is wrong, others compensate**. Key facts up front : ![World map with weather models used in SteadyMet](https://steady-sun.com/wp-content/uploads/2026/07/carte-modeles-meteo-fond-transparent.png) Multimodel weather forecasting sources | Forecast horizons | 0–30 min (nowcasting) and 30 min–3 h (intraday) for solar only D+1–D+15 (day-ahead & medium-range) for both solar and wind | | --- | --- | | Update frequency | Every 5–15 min (satellite-based) / up to every hour (NWP-based) | | NWP models aggregated | 20+ including IFS, AIFS, GFS, CAMS, ICON, ICON-EU, ICON-D2, ICON-2I, ARPEGE, ARPEGE-EU, AROME, HRRR, HRDPS, GDPS, WRF, HARMONIE-NEA and more. | | NWP spatial resolution | 10–50 km (global) 5–10 km (regional high resolution) 1–5 km (regional very high resolution) down to 1 km (regional WRF) | | Geostationary satellites | 5 systems including GOES West, GOES East, Meteosat 0°, Meteosat IODC, and Himawari. | | Satellite spatial resolution | 500 m – 3 km | | Satellite update cadence | Every 5–15 minutes | | Technologies covered | PV (fixed, tracking, bifacial), CSP, onshore wind, offshore wind | | Probabilistic output | 11 quantiles: P00, P10, P20 … P90, P100 | ********[](https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model)[](https://www.ecmwf.int/en/forecasts/datasets/aifs-machine-learning-data)[](https://vlab.noaa.gov/web/gfs)[](https://www.ecmwf.int/en/about/what-we-do/environmental-services/copernicus-atmosphere-monitoring-service)[](https://www.dwd.de/EN/research/weatherforecasting/num_modelling/01_num_weather_prediction_modells/icon_description.html)[](https://meteofrance.com/meteo-a-z/les-modeles-de-prevision-meteo)[](https://meteofrance.com/meteo-a-z/les-modeles-de-prevision-meteo)[](https://rapidrefresh.noaa.gov/hrrr/)[](https://open.canada.ca/data/en/dataset/5b401fa0-6c29-57f0-b3d5-749f301d829d)[](https://open.canada.ca/data/en/dataset/c041e79a-914a-5a4e-a485-9cbc506195df)[](https://ncar.ucar.edu/what-we-offer/models/weather-research-and-forecasting-model-wrf)[](https://www.dmi.dk/friedata/dokumentation/data/forecast-data-weather-model-harmonie)[](https://www.nesdis.noaa.gov/our-satellites/currently-flying/geostationary-satellites)[](https://www.nesdis.noaa.gov/our-satellites/currently-flying/geostationary-satellites)[](https://user.eumetsat.int/data/satellites/meteosat-second-generation/0-degree-service)[](https://user.eumetsat.int/data/satellites/meteosat-second-generation/indian-ocean-data-coverage)[](https://www.jma.go.jp/jma/jma-eng/satellite/index.html) --- ## From minutes to weeks ## How Steadysun splits the forecasting timeline As seen above, **no single technology covers the full forecasting timeline**. At Steadysun, we split it across two engines, each taking the lead at a different horizon: **Steadysat for the short range**, **Steadymet for the long range**. t-10min – 30 minutes **Nowcasting** (Steadysat) 30 minutes – 6 hours **Intraday** (Steadysat + Steadymet blend) 6 hours – D+15 **Day-ahead & Medium range** (Steadymet) ### Nowcasting with Steadysat **[Steadysat](https://steady-sun.com/steadysat-intraday-forecasts/) is Steadysun’s implementation of the satellite-based approach **described above. At very short time scales, the dominant uncertainty is cloud motion, and NWP models are too coarse and too slow to capture a cloud front moving across a solar farm in the next 15 minutes. Steadysat solves this using **cloud motion vectors (CMV)** derived from geostationary satellite imagery. By tracking cloud displacement between successive satellite images (refreshed every 5 to 15 minutes at 500 m–2 km spatial resolution) **Steadysat extrapolates where clouds will be in the next 30 minutes** with high spatial precision. The 5 satellites feeding Steadysat are: - **NOAA GOES-W** — Western Americas and Eastern Pacific - **NOAA GOES-E** — Eastern Americas and Western Atlantic - **EUMETSAT MSG-0°** — Europe, Africa, and the Atlantic - **EUMETSAT MSG-IODC** — Indian Ocean and East Africa - **JMA HIMAWARI** — Asia-Pacific ![World map showing satellite coverage areas](https://steady-sun.com/wp-content/uploads/2026/07/carte-satellite-sansgrille.png) Space-based observation network Steadysat also ingests real-time in-situ measurements (irradiance sensors, pyranometers, power meters, SCADA data) to continuously calibrate its outputs at the plant level. ![NASA Terra MODIS satellite image](https://steady-sun.com/wp-content/uploads/2025/06/terra-modis-satellite-image-nasa.webp) Satellite imagery of Earth from space ### Intraday forecasting with Steadysat + Steadymet blend Beyond 30 minutes, cloud motion vectors alone lose predictive skill as cloud systems evolve and dissipate. [**Steadysat**](https://steady-sun.com/steadysat-intraday-forecasts/) extends its horizon to 6 hours by** blending CMV-based extrapolation with the forecast from [Steadymet](https://steady-sun.com/steadymet-day-ahead-forecasts/), our proprietary hybrid multi-model engine**, weighting each source according to its demonstrated skill at each time step. This blended approach is updated every 5 to 15 minutes, making it ideal for [**intraday market participation**](https://steady-sun.com/renewable-energy-trading-portfolio-management/), storage dispatch, and [**island grid balancing**](https://steady-sun.com/insular-system-management/). ### Day-ahead and medium-range forecasting with Steadymet **For horizons beyond 6 hours, [Steadymet](https://steady-sun.com/steadymet-day-ahead-forecasts/) takes over**. It aggregates output from more than 20 global and regional NWP models and uses machine learning to eliminate the systematic biases of each individual model (the same principle described above in the hybrid multi-model approach), here** run continuously in production for every plant**. Global NWP models such as GFS and ECMWF run at 10–25 km horizontal resolution. For sites where local topography, coastal effects, or island microclimates matter, Steadysun can deploy the regional **WRF (Weather Research and Forecasting) model at 1 km resolution**, implemented and optimised by our in-house team of meteorologists. **Steadymet delivers forecasts up to 15 days ahead, updated up to every hour**. ![World map with weather models used in SteadyMet](https://steady-sun.com/wp-content/uploads/2026/07/carte-modeles-meteo-fond-transparent-e1784620140361.png) Multimodel weather forecasting sources **[Steadysat](https://steady-sun.com/steadysat-intraday-forecasts/) is Steadysun’s implementation of the satellite-based approach **described above. At very short time scales, the dominant uncertainty is cloud motion, and NWP models are too coarse and too slow to capture a cloud front moving across a solar farm in the next 15 minutes. Steadysat solves this using **cloud motion vectors (CMV)** derived from geostationary satellite imagery. By tracking cloud displacement between successive satellite images (refreshed every 5 to 15 minutes at 500 m–2 km spatial resolution) **Steadysat extrapolates where clouds will be in the next 30 minutes** with high spatial precision. The 5 satellites feeding Steadysat are: - **NOAA GOES-W** — Western Americas and Eastern Pacific - **NOAA GOES-E** — Eastern Americas and Western Atlantic - **EUMETSAT MSG-0°** — Europe, Africa, and the Atlantic - **EUMETSAT MSG-IODC** — Indian Ocean and East Africa - **JMA HIMAWARI** — Asia-Pacific Steadysat also ingests **real-time in-situ measurements** (irradiance sensors, power meters) to continuously calibrate its outputs at the plant level. ![World map showing satellite coverage areas](https://steady-sun.com/wp-content/uploads/2026/07/carte-satellite-sansgrille.png) Space-based observation network Beyond 30 minutes, cloud motion vectors alone lose predictive skill as cloud systems evolve and dissipate. [**Steadysat**](https://steady-sun.com/steadysat-intraday-forecasts/) extends its horizon to 6 hours by** blending CMV-based extrapolation with the forecast from [Steadymet](https://steady-sun.com/steadymet-day-ahead-forecasts/), our proprietary hybrid multi-model engine**, weighting each source according to its demonstrated skill at each time step. This blended approach is updated every 5 to 15 minutes, making it ideal for [**intraday market participation**](https://steady-sun.com/renewable-energy-trading-portfolio-management/), storage dispatch, and [**island grid balancing**](https://steady-sun.com/insular-system-management/). ![Twelve animated satellite simulations for ESA project](https://steady-sun.com/wp-content/uploads/2025/08/european-space-agency-and-steadysun-project-satellite-imagery.gif) Advanced multi-satellite forecasting **For horizons beyond 6 hours, [Steadymet](https://steady-sun.com/steadymet-day-ahead-forecasts/) takes over**. It aggregates output from more than 20 global and regional NWP models and uses machine learning to eliminate the systematic biases of each individual model (the same principle described above in the hybrid multi-model approach), here** run continuously in production for every plant**. Global NWP models such as GFS and ECMWF run at 10–25 km horizontal resolution. For sites where local topography, coastal effects, or island microclimates matter, Steadysun can deploy the regional **WRF (Weather Research and Forecasting) model at 1 km resolution**, implemented and optimised by our in-house team of meteorologists. **Steadymet delivers forecasts up to 15 days ahead, updated up to every hour**. ![World map with weather models used in SteadyMet](https://steady-sun.com/wp-content/uploads/2026/07/carte-modeles-meteo-fond-transparent-e1784620140361.png) Multimodel weather forecasting sources --- ## The full forecasting pipeline ### 5 steps from raw data to delivered forecast #### Plant virtual model #### Data acquisition #### Baseline calibration #### Real-time model optimisation #### Forecast delivery Before any forecast can be tailored to your site, you provide the technical details needed **to build a high-fidelity digital representation of your asset**. - **For solar**: panel technology (monocrystalline, polycrystalline, thin-film, bifacial), tilt and azimuth, installed capacity (kWp or MW), tracker configuration (fixed, single-axis, dual-axis), inverter specs and clipping behaviour.  - **For wind**: surface roughness, obstacle height (trees or buildings affecting wind displacement), and the position and type of each individual turbine.  Once your site is set up, forecasting can begin.  ![solar-panels-field-wind-turbines-horizon-renewable-green-energy](https://steady-sun.com/wp-content/uploads/2026/09/solar-panels-field-wind-turbines-horizon-renewable-green-energy.webp) **Every forecast cycle start with data ingestion** from multiple independent sources: **Satellite observations (Steadysat)** - Live images from GOES-W, GOES-E, MSG-0°, MSG-IODC, and HIMAWARI - Multiple spectral channels: visible, near-infrared, thermal infrared - Cloud cover, aerosol optical depth, atmospheric water vapour - Cadence: every 5–15 minutes; resolution: 500 m–2 km **NWP model output (Steadymet)** - 20+ global and regional models, refreshed at their native update cycles - Parameters: solar irradiance, wind speed and direction at multiple levels, temperature, humidity, pressure, cloud cover, precipitation, aerosols, ozone - Global resolution: 10–25 km; regional WRF: down to 1 km **Ground measurements** - Irradiance sensors (GHI, DNI, DHI), pyranometers, power meters, SCADA data, used for real-time calibration and model adaptation Once your site is onboarded, where historical production data is available (typically at least several months of SCADA or meter data), Steadymet and Steadysat are trained on the relationship between observed weather and observed power. **The system learns the plant’s specific behaviour** and corrects systematic biases (soiling losses, inverter clipping, wake effects, local aerosol climatology). **For new installations** without production history, Steadysun’s [**Hindcast service**](https://steady-sun.com/historical-weather-and-power-forecast-data/) can provide a solution. Hindcast runs the current Steadymet and Steadysat models on historical weather periods at your specific site, simulating what the forecasting system would have predicted in the past. This establishes a rigorous performance baseline (including MAE, RMSE, and full quantile distributions) before live operation begins. It’s particularly valuable for [**financial model validation**](https://steady-sun.com/project-development/), PPA negotiations, and BESS sizing. During live operation, **Steadymet and Steadysat continuously ingest real-time plant measurements** to update their site-specific correction layers. If the forecast is running 5% high this morning, the system adjusts. Machine learning algorithms detect emerging biases, for example, a systematic underestimation of morning irradiance due to coastal fog, and correct them dynamically. This continuous learning loop means forecast accuracy typically improves over the first weeks and months of operation as the models accumulate site-specific knowledge. ![Four quadrants of probabilistic GHI forecasts (clear/cloudy, with/without correction)](https://steady-sun.com/wp-content/uploads/2023/06/image-1.webp) Improved forecast with live correction Raw meteorological forecasts are converted into AC or DC power output using the plant’s physical model, with high-resolution topographical corrections down to 90 m resolution. Forecasts are then delivered via **API, SFTP, CSV, NetCDF or Dashboard**, along with their uncertainty metrics, at time steps down to 1 minute. Quality is tracked continuously using three standard metrics:  - **MAE (Mean Absolute Error)**: average magnitude of the error, in the same units as power - **MBE (Mean Bias Error)**: systematic over- or under-forecasting tendency - **RMSE (Root Mean Squared Error)**: penalises large errors more heavily than MAE, relevant for grid operators who care about tail events ![Graph showing a 24-hour SteadyMet solar power forecast (red line) against actual measured production (blue line) for a single day, with confidence intervals, illustrating forecast accuracy.](https://steady-sun.com/wp-content/uploads/2025/06/PAC_dayahead.png) Power forecast from SteadyMet, showcasing the accuracy of our day-ahead (D+1) predictions against actual measurements. ![Steadysun day-ahead AC power forecast for onshore wind farm](https://steady-sun.com/wp-content/uploads/2025/06/Dayhead_pac_wind.png) Steadysun day-ahead AC power forecast with quantiles: example for an onshore wind farm ## Experience our energy forecasting API **Put our forecasts to the test!** Easily integrate our data into your tools and see the impact firsthand. Optimize** two plants free** with Expert+ features – no commitment ! [Let's get started!](https://steadyweb.steady-sun.com/account/signup/) --- ## Quantify uncertainty, reduce risk ## Probabilistic forecasting Weather is inherently uncertain. Instead of relying on a single forecast, Steadysun captures that uncertainty with **probabilistic forecasts** that show the full range of possible outcomes. **Each forecast point includes 11 quantiles** (P00, P10, P20, P30, P40, P50, P60, P70, P80, P90, P100), giving you a clearer picture of what to expect and how uncertain the forecast is. What these mean in practice: - P50 is the median forecast, as likely to be too high as too low. This is the single best-guess value if you can only use one number. - P10 is the pessimistic bound, actual production will exceed this 90% of the time. A conservative floor for planning. - P90 is the optimistic bound, actual production will fall below this 90% of the time. A conservative ceiling for planning. - P00 and P100 represent the full envelope of plausible scenarios. In practice,** the P10–P90 interval gives a robust 80% confidence band**. The P00 and P100 bounds represent the plausible worst and best cases, useful for stress-testing storage dispatch strategies or estimating maximum imbalance exposure. ![Animated Steadysun Steadymet temperature forecast with and without quantiles](https://steady-sun.com/wp-content/uploads/2025/06/example-of-SteadyMet-temperature-forecast-with-and-without-quantiles.gif) Example of Steadysun's Steadymet temperature forecast showing forecast uncertainty with quantiles ### Where the uncertainty comes from These quantiles are generated  using a combination of three complementary sources. ### Physical ensembles Propagating uncertainty from the spread across 20+ NWP models. ### Statistical post-processing Learning the empirical distribution of forecast errors at each site and horizon. ### Satellite-based estimation Using cloud cover variability within the satellite footprint to modulate short-term uncertainty. ### Beyond quantiles: scenarios While quantiles provide a range of possible outcomes for each hour, they don’t show how these outcomes might evolve over time. Scenario generation creates full, realistic time-series simulations, complete and internally consistent trajectories that span the entire forecast horizon. This means you don’t just see isolated worst- or best-case moments;** you see how conditions could unfold hour by hour**. ![Live solar power forecast including peak and quantile range](https://steady-sun.com/wp-content/uploads/2025/04/steadyweb-pac-forecast-overview.png) Live forecast with peak power and quantiles ![Case study booklet on hindcasting for project development](https://steady-sun.com/wp-content/uploads/2025/04/hindcast-case-study.webp) ### A real-world example In 2024, a developer planning [**a hybrid PV+BESS plant in northern Mauritius**](https://steady-sun.com/blog/case-study/reducing-costs-and-risks-in-solar-projects-the-role-of-hindcast-data-for-project-developers/) used Steadysun’s probabilistic hindcast to size their battery and estimate imbalance penalties before commissioning. The result was a **15–31% improvement over persistence forecasts** across different time horizons, and full compliance with the local Grid Code. Ignoring uncertainty doesn’t make it go away. It just means someone else is bearing it (usually the grid). --- ## THE FULL PICTURE ## Forecast parameters catalogue The table below lists all weather and production parameters available. Parameter Short name Description Unit AC power pac Solar AC power at inverter output W DC power pdc Solar DC power at array output W Global Horizontal Irradiance ghi Total solar irradiance on a horizontal surface W·m⁻² Direct Normal Irradiance dni Direct beam irradiance on a surface normal to the sun W·m⁻² Diffuse Horizontal Irradiance dhi Diffuse irradiance on a horizontal surface W·m⁻² Global Tilted Irradiance gti Total irradiance on a tilted (panel) surface W·m⁻² Beam Normal Irradiance bni Beam irradiance component, normal incidence W·m⁻² Beam Horizontal Irradiance bhi Beam irradiance on a horizontal surface W·m⁻² Photosynthetically Active Radiation par Solar radiation in the 400–700 nm band W·m⁻² Parameter Short name Description Unit High cloud cover hcc Fraction of sky covered by high-level clouds [0–1] Medium cloud cover mcc Fraction of sky covered by mid-level clouds [0–1] Low cloud cover lcc Fraction of sky covered by low-level clouds [0–1] Total cloud cover tcc Total fraction of sky covered by clouds [0–1] Precipitation rate mtpr Liquid water precipitation rate mm·h⁻¹ Relative humidity rh2m Relative humidity at 2 m above ground % Specific humidity q Mass of water vapour per unit mass of moist air g·kg⁻¹ Total column water vapour tcwv Integrated atmospheric water vapour column kg·m⁻² Parameter Short name Description Unit Ambient temperature t2m Air temperature at 2 m above ground °C Dewpoint temperature td Dewpoint temperature at 2 m °C PV cell temperature t_cell Estimated PV module cell temperature °C Parameter Short name Description Unit Wind speed at 10 m 10m_wind_speed Mean wind speed 10 m above ground m·s⁻¹ Wind direction at 10 m 10m_wind_direction Wind direction at 10 m (0° = North) ° Wind gust at 10 m 10m_gust Maximum wind gust speed at 10 m m·s⁻¹ Wind speed at hub height hhws Wind speed at turbine hub height (25–100 m) m·s⁻¹ Mean sea level pressure mlsp Atmospheric pressure reduced to sea level hPa Surface pressure msp Atmospheric pressure at the surface hPa If your workflow requires additional variables, our team can add them to your forecast outputs to meet your specific operational needs. [Talk to an expert](https://steady-sun.com/contact-us/) --- ## Delivery and Integration ## Getting the forecast into your systems should be frictionless ![Laptop displaying the Steadyweb dashboard featuring a PV system overview, interactive map, and a wind speed graph with shaded probabilistic confidence intervals.](https://steady-sun.com/wp-content/uploads/2025/11/steadyweb-interface-steadysun-forecasting-platform.webp) We support: - REST API (JSON) , the standard integration path, with token authentication and comprehensive documentation at steadyweb.steady-sun.com/rapidoc/ - CSV and XML exports for teams working with spreadsheet-based workflows or legacy systems. - SFTP push delivery with scheduled file transfers directly to your server. - SCADA connectors enabling seamless integration with major energy management and SCADA platforms. - Steadyweb portal , our web interface for visualization and manual review of forecasts and measured production history. --- ## BUILT FOR YOUR NEEDS ## Supported technologies and use cases Our solution is technology-agnostic and covers: - **Solar PV** – fixed-tilt, single-axis tracking, dual-axis tracking, bifacial - **Concentrated Solar Power (CSP)** – parabolic trough, tower - **Onshore wind** – individual turbines and wind farms - **Offshore wind** – with marine boundary layer corrections - **Hybrid systems** – PV+BESS, wind+BESS, multi-technology portfolios Use cases span the full project lifecycle: Phase Use case Research & innovation Forecasting method benchmarking, academic collaboration, access to historical datasets Development Hindcast-based performance baseline, PPA validation, BESS sizing Construction Pre-commissioning model setup, satellite calibration Operations Real-time dispatch, grid code compliance, O&M scheduling Trading Day-ahead bidding, intraday optimisation, imbalance cost reduction Grid management Reserve scheduling, curtailment management, frequency regulation Self-consumption Load-matching optimisation, battery charge/discharge scheduling, export minimisation ![Solar panels and wind turbines field](https://steady-sun.com/wp-content/uploads/2026/09/bernd-dittrich-gFud7-XmQvk-unsplash.webp) --- ## Your questions answered ## Common questions The core methodology is the same: NWP models + AI multi-model engine + probabilistic output. The physics are different. [**Solar power forecasting**](https://steady-sun.com/solar-energy-forecasts/) is driven by irradiance and cloud cover; [**wind energy forecasting**](https://steady-sun.com/wind-energy-forecasts/) is driven by wind speed and direction at hub height, atmospheric stability, and turbulence. Wind is also more sensitive to local terrain effects; a ridge or valley can accelerate or deflect wind in ways that a global NWP model at 10–25 km resolution can’t fully resolve, which is why site-specific calibration is especially important for wind. Solar has an additional intraday tool (Steadysat, using geostationary satellites) that has no direct equivalent for wind. [**Steadysat**](https://steady-sun.com/steadysat-intraday-forecasts/) is Steadysun’s satellite-based nowcasting and intraday forecasting product. It processes live imagery from 5 geostationary satellites to track cloud motion and forecast solar irradiance up to 6 hours ahead, updated every 5–15 minutes. [**Steadymet**](https://steady-sun.com/steadymet-day-ahead-forecasts/) is the multi-model NWP-based product for day-ahead and medium-range forecasting (up to 15 days), aggregating 20+ weather models and applying AI-based bias correction. **The two products are complementary** and together cover the full 0–15 day horizon. Forecast accuracy is measured using MAE, MBE, and RMSE, and **depends on three factors**: - climate (cloud variability, aerosol load), - installation type, - and forecast horizon. Short-horizon forecasts (intraday, up to 3 h) are generally more accurate than day-ahead or medium-range forecasts. What we can say: in one deployment, **better solar forecasting cut grid code penalties by 50% and curtailment losses by 75%** — *results specific to that site and not a guarantee for every project*. [Contact us for a site-specific assessment](https://steadyweb.steady-sun.com/account/signup/) **Yes**. Steadysat’s global satellite coverage and Steadymet’s multi-model approach both operate worldwide. Steadysun has specific experience with** island and insular grids**, including [**Tahiti**](https://steady-sun.com/blog/steadysun-news/steadysun-solar-forecasts-contribute-50-renewables-tahiti/), [**Mauritius**](https://steady-sun.com/blog/case-study/reducing-costs-and-risks-in-solar-projects-the-role-of-hindcast-data-for-project-developers/), and New Caledonia, where forecasting challenges are amplified by limited grid inertia and high renewable penetration. The regional WRF model at 1 km resolution is particularly effective in these environments. Every Steadysun forecast includes **11 probabilistic quantiles** (P00 to P100), generated from the spread across NWP ensemble members and from site-specific error distributions. These quantiles allow users to set risk-adjusted bidding strategies, size storage systems under different confidence levels, and comply with grid codes that require uncertainty-aware production schedules. #### Do you still have questions? ## Contact us , we will be happy to answer you!