Solar Irradiance Portal

Standard satellite datasets can overestimate solar potential in Uganda, leading to financial risk. Our AI platform corrects these inaccuracies, delivering reliable solar irradiance data fine-tuned for the region.

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Closing the Data Gap

Standard satellite datasets consistently overestimate solar potential in Uganda. Our AI model corrects this bias, providing bankable, high-accuracy estimates that align with ground measurements.

The Problem: Satellite Data Bias

This chart shows the significant gap between actual ground measurements and uncorrected satellite data for Arua, Uganda.

Our Solution: AI-Powered Correction

Our model's predictions align closely with ground truth data, correcting the bias and providing a far more reliable estimate.

A Powerful Analytics Platform

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Satellite Data Correction

We integrate and correct data from sources like CAMS and NASA POWER, addressing the systematic positive bias found in raw satellite estimates for Uganda.

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AI-Powered Accuracy

Our Random Forest model leverages advanced machine learning to generate highly accurate solar irradiance forecasts and optimize your renewable energy planning strategies.

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Interactive Visualizations

Explore bias-corrected data through an intuitive interface with dynamic charts and an interactive map, making complex solar patterns easy to analyze.

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Seamless API Access

Integrate our corrected solar data directly into your applications with a robust RESTful API, empowering developers with bankable-grade information for any location in Uganda.

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Regionally Tuned for Uganda

Our models are specifically designed for Uganda's unique climate zones and topography, providing the localized accuracy needed for precise energy assessments.

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On-Demand Analysis

Get instant access to our fine-tuned historical dataset through the web portal, ensuring you have reliable data for critical decision-making when you need it.

From Inaccurate Data to Bankable Insights

The success of solar energy in Uganda is hampered by inaccurate satellite data, which can overestimate solar potential and introduce significant financial risk to projects. This leads to incorrect system sizing and can reduce lifetime energy savings for consumers by 5-20%.

Our platform was developed to solve this problem. By training AI models on ground-truth measurements from across Africa, we systematically correct these satellite biases. We provide the reliable, localized data needed to de-risk investments, optimize project design, and unlock Uganda's true solar potential.

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