Resource Modeling

Unlock the full potential of renewable energy with our cutting-edge tool, providing you with accurate and reliable estimates of solar and wind energy generation. Make informed decisions and maximize your investment with confidence, no matter where you are located.

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The need for high-resolution spatiotemporal data in RE Projects

To optimize the performance of wind and solar projects and ensure feasibility, obtaining high-resolution spatiotemporal data is an essential element. This data enables project developers to gain a comprehensive understanding of local wind and solar conditions, including their intensity and variability. By analyzing this data, developers can accurately determine the project size, configuration, and expected energy production and cost, while considering potential impacts on the local environment and community, such as noise and visual disturbances. This ensures that the project is operated in a way that minimizes negative effects while maximizing community benefits.

The importance of high-resolution spatiotemporal data goes beyond project development; it is critical to optimizing project operation as well. With access to energy production data, developers can adjust output to match changing electricity demand or optimize energy storage systems, thereby enhancing the value of the energy produced. Our advanced technology provides the necessary data for making informed decisions, improving project performance, and benefiting both your business and the community. With our data, you can unlock the full potential of your projects, ensuring they are successful and profitable.

Our input datasets

ERA 5

ERA 5 offers hourly data on various atmospheric, land-surface, and sea-state parameters with uncertainty estimates.

NOAA Groundtruth Data

The ground-based observation datasets provided by NOAA are utilized to correct the bias in ERA5 specific humidity and temperature.

NIWE Groundtruth Data

The ground-based observation datasets provided by NIWE Solar Radiation Resource Assessment are utilized to enhance the accuracy of solar irradiance values.

Our modeling techniques

We utilize a hybrid model that integrates satellite and ground-based station observations and employs advanced machine learning models to determine precise irradiance values by leveraging ERA5 data and ground-based observation datasets.

Kriging

The GRE model is designed to interpolate spatial data, allowing for predictions of unsampled locations based on ground observations. To achieve this, the model uses a technique called kriging, which enables the downscaling of key parameters such as Specific Humidity, Temperature, and Solar Irradiance.

Bilinear Interpolation

The GRE model uses bilinear interpolation to downscale geopotential data. This technique involves calculating the value of a grid cell based on the weighted average of nearby grid cells, with the weights determined by the distance between the cells.

Machine Learning

In order to correct for biases in data, computational algorithms called Artificial Neural Networks (ANN) are used. The GRE model incorporates ANN to determine bias-corrected values of parameters such as SSRD, Specific Humidity, and Temperature.

Unleash your project's full potential with our comprehensive resource modeling solutions

Our comprehensive resource modeling solution incorporates wind resource assessment, solar resource assessment, solar energy modeling, wind turbine modeling, and solar panel assessment. We provide 20 years of historical data on key parameters such as Diffused Horizontal Irradiance, Direct Normal Irradiance, Global Horizontal Irradiance, and more. Our data enables accurate analysis of Albedo, Aerosol Optical Depth, Wind Speeds, Temperatures, and Precipitation in your region. Our high-resolution data is available at 5km intervals, and we can even generate customized datasets for multiple resources.

You can trust us to provide reliable data that empowers you to make informed decisions and maximize the potential of your projects.