Resource Modeling
Estimate solar energy generation at any location at different confidence levels utilizing reanalyzed historical resource datasets.
The need for high-resolution spatiotemporal data
The process to determine a great site for a wind or solar project is highly complex. The lack of a global harmonized geo-mapped assessment of wind and solar resources adds to this, and therefore, there is a need for a high-resolution spatiotemporal assessment. Such assessment can accurately represent the geographical variability of the built environment along with impacts of seasonal changes in wind or solar resources.
Reduced Data Variability
RE resources are highly variable and depend on several factors such as geographical and temporal factors
Enhanced Energy Assessment
Estimate accurate energy yields from RE systems in specific locations
Site Assessment
Determine RE System output variability, feasibility, and viability
Risk Management
Determine the risk factors that affect the O&M and longevity of RE projects
Forecast
Gauge metrological and climatology limitations of the site to Create accurate financial models
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.
