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ABOUT
Up to the next 15 days, for day-ahead and intraday forecasting need
SteadyMet provides weather and production forecasts up to 15 days ahead. This product combines several sources of Numerical Weather Predictions (NWP) data with physical models and artificial intelligence.
SteadyMet can be configured at very high resolution (1 km) using the Weather Research and Forecasting (WRF) model, providing highly accurate forecasts at local scale. Steadysun is able to implement and optimize this model anywhere in the world to meet the need of high-quality day-ahead forecasts.
Day-ahead power forecast for a 30 MWp distributed PV portfolio
(tropical island)
Day-ahead GHI forecasts for 1 site (mid-latitude oceanic climate)
Multi-model GHI forecast above subtropical islands
Production & forecast history (data over one year)
Up to 1 hour
Update frequency
1 min
Forecast time-step
Power, GHI, DNI, DHI, GTI, Temperature, Wind Speed, Wind Direction, etc.
Available parameters
Site, Portfolio, City, Region or Country
Coverage
PV, CSP, onshore, offshore
Technology
API, SFTP, etc.
Data delivery
P10, P20,…, P80, P90
Confidence levels
KEY BENEFITS
WORLDWIDE COVERAGE
Thanks to a large number of global and regional NWP data from several weather operators.
BEST-IN-CLASS SOLUTION
An approach combining ensemble predictions from the leading weather models, real-time on-site measurements and cutting-edge technologies to offer accurate probabilistic forecasts.
RELEVANT FOR MICROCLIMATES
An in-house regional model at very high spatio-temporel resolution, providing realistic and precise forecasts in areas where local effects are significant and public regional weather models are not available.
TAILORED OUTPUTS
In terms of weather parameters, update frequency, granularity and format.
METHODOLOGY
Step 1
DATA ACQUISITION
From several external and internal sources
Global and Regional Numerical Weather Prediction (NWP) models
Numerous parameters (clouds, radiation, wind speed/direction, temperature, aerosols, etc.)
Step 2
MODELING
Optimal combination of NWP models’ outputs
Power modeling based on physical models and plant features
High-resolution topographical corrections (down to 90m)
Probabilistic forecasting using physical and statistical approaches
Step 3
OPTIMIZATION
Based on historical and/or real-time on-site measurements
Continuous accuracy improvements using state-of-the-art machine learning techniques
To take into account local weather phenomena and power plants’ behavior
Step 4
DELIVERY
Flexible sending (API, SFTP, etc..)
Customized format (csv, txt, etc.)
Dedicated and secured web interfaces (visualization, data analytics and warnings)
Forecast performance monitoring
DATA ACQUISITION
MODELING
OPTIMIZATION
DELIVERY
From several external and internal sources
Global and Regional Numerical Weather Prediction (NWP) models
Numerous parameters (clouds, radiation, wind speed/direction, temperature, aerosols, etc.)
Optimal combination of NWP models’ outputs
Power modeling based on physical models and plant features
High-resolution topographical corrections (down to 90m)
Probabilistic forecasting using physical and statistical approaches
Based on historical and/or real-time on-site measurements
Continuous accuracy improvements using state-of-the-art machine learning techniques
To take into account local weather phenomena and power plants’ behavior
Flexible sending (API, SFTP, etc..)
Customized format (csv, txt, etc.)
Dedicated and secured web interfaces (visualization, data analytics and warnings)
Forecast performance monitoring
