Resource Modeling
Estimate solar and wind energy generation at any location at different confidence levels utilizing reanalyzed historical resource datasets.
The need for high-resolution spatiotemporal data in RE Projects
There is a need for high-resolution spatiotemporal data for wind and solar projects because this type of data can provide important information about the potential performance and feasibility of the project. For example, high-resolution data can help project developers understand the local wind and solar conditions at a particular site, including the intensity and variability of the wind and solar resources. This can be important for determining the size and configuration of the project, as well as the expected energy production and cost.
In addition, high-resolution spatiotemporal data can help project developers understand the potential impacts of the project on the local environment and community, including any potential noise or visual impacts. This information can be used to design and operate the project in a way that minimizes negative impacts and maximizes benefits for the community.
Finally, high-resolution spatiotemporal data can be used to optimize the operation of the project once it is up and running. For example, data can be used to adjust the output of the project to match the changing demand for electricity or to optimize the use of energy storage systems to maximize the value of the energy produced.
Our input datasets
ERA 5
ERA 5 is comprehensive reanalysis data. It consists of observation sets providing hourly data on many atmospheric, land-surface, and sea-state parameters together with estimates of uncertainty
NOAA Groundtruth Data
Ground-based observation datasets from NOAA are used for bias correction of ERA5 Specific Humidity and Temeperature
NIWE Groundtruth Data
Ground-based observation datasets from NIWE Solar Radiation Resource Assessment are used for to determine more accurate solar irradiance values
Our modeling techniques
We work with a hybrid model integrating satellite and ground-based station observations. We also use advanced machine learning models to determine accurate irradiance values through the use of ERA5 data and ground-based observation data sets.
Kriging
For interpolating spatial data that makes a prediction of an unsampled location using ground observation. GRE model uses kriging for downscaling of Specific Humidity, Temperature, Solar Irradiance etc.
Bilinear Interpolation
For calculating values of a grid location based on nearby grid cells. Bilinear interpolation correlates output cell value to weighted average and distance. GRE model uses this technique for downscaling of geopotential.
Machine Learning
Artificial Neural networks (ANN) or neural networks are computational algorithms used for bias correction. GRE model using ANN is used to determine bias corrected values of SSRD, Specific humidity, and temperatures
Comprehensive modeled output for further analysis
With our resource modeling, we deliver historical data of the past 20 years for Diffused Horizontal Irradiance (GHI) [W/𝒎^𝟐], Direct Normal Irradiance (DNI)[[W/m^2], Global Horizontal Irradiance (DHI) [W/𝒎^𝟐], Albedo Analysis, Aerosol Optical Depth, Wind Speeds[m/s], Temperatures[C], and Precipitation[mm].
The specific irradiance values are available at 5km resolution for all regions as per our coverage, whereas we can generate customized resolution datasets for multiple resources.
