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CLOUD & WEATHER OBSERVATION
SOLAR ENERGY FORECASTING
CLOUD & WEATHER OBSERVATION
SOLAR ENERGY FORECASTING
En
CLOUD & WEATHER OBSERVATION
SOLAR ENERGY FORECASTING
CLOUD & WEATHER OBSERVATION
SOLAR ENERGY FORECASTING
En
ABOUT
Forecasts based on meteorological models for day-ahead solar forecasting needs
SteadyMet provides weather and solar 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 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)
4 times a day
Update frequency
1 min
Forecast time-step
Power, GHI, DNI, DHI, GTI, Temperature, Wind, etc.
Available parameters
Site, Portfolio, City, Region or Country
Coverage
PV, Trackers, Bifacial, CSP
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 services.
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
SteadyMet is based on an optimal combination of several Numerical Weather Prediction (NWP) models. These models simulate the evolution of the atmosphere (step 2) from initial atmospheric conditions estimated by assimilation of meteorological observation data over a global or local granularity (step 1). For this purpose, the area of calculation is divided into a three-dimensional grid with more or less large mesh.
Weather model’s outputs of interest (solar radiation, temperature, wind, etc.) are then optimized (step 3), by using production/irradiance observations, to take into account local phenomena, and by leveraging artificial intelligence technologies.
Ready-to-use forecasts, in a customized format, are then disseminated (step 4) through our proprietary web interface, csv files delivered via (S)FTP platforms or API.
Step 1
DATA ACQUISITION
From several external and internal sources.
Global and Regional Numerical Weather Prediction (NWP) models.
Numerous parameters (clouds, radiation, temperature, wind, aerosols, etc.)
Step 2
MODELING
Optimal combination of NWP models’ outputs.
Accurate estimation of clear sky conditions using real-time aerosols prediction.
PV 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 delivery (API, SFTP, etc..).
Customized format (csv, txt, etc.).
Dedicated and secured web interfaces (visualization, data analytics and warnings).
Forecast performance monitoring.
METHODOLOGY
SteadyMet is based on an optimal combination of several Numerical Weather Prediction (NWP) models. These models simulate the evolution of the atmosphere (step 2) from initial atmospheric conditions estimated by assimilation of meteorological observation data over a global or local granularity (step 1). For this purpose, the area of calculation is divided into a three-dimensional grid with more or less large mesh.
Weather model’s outputs of interest (solar radiation, temperature, wind, etc.) are then optimized (step 3), by using production/irradiance observations, to take into account local phenomena, and by leveraging artificial intelligence technologies.
Ready-to-use forecasts, in a customized format, are then disseminated (step 4) through our proprietary web interface, csv files delivered via (S)FTP platforms or API.
DATA ACQUISITION
MODELING
OPTIMIZATION
DELIVERY
From several external and internal sources.
Global and Regional Numerical Weather Prediction (NWP) models.
Numerous parameters (clouds, radiation, temperature, wind, aerosols, etc.)
Optimal combination of NWP models’ outputs.
Accurate estimation of clear sky conditions using real-time aerosols prediction.
PV 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 delivery (API, SFTP, etc..).
Customized format (csv, txt, etc.).
Dedicated and secured web interfaces (visualization, data analytics and warnings).
Forecast performance monitoring.
