Projects / Top Talent Project

Map Solar Penetration and Productive Uses of Renewable Energy in Kenya

Project Kickoff: June 4


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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at https://omdena.com/projects.

The problem

Kenya, like many countries in the Global South, faces significant challenges in ensuring broad and equitable access to energy, particularly renewable energy like solar power. The lack of detailed, localized data on energy supply and demand hinders the effective planning and implementation of energy solutions that could serve underserved communities and spur socio-economic growth. Existing gaps in data prevent a thorough understanding of where energy access is lacking and where renewable energy solutions, such as solar home systems, can be most effectively implemented to boost productivity, especially in sectors like agriculture.

Impact of the Problem:

  • Inadequate Energy Planning: Without detailed and accurate data on solar penetration and the potential areas for productive energy use, energy planning does not capture accurately neither the current supply of solar home systems nor the potential demand for renewable energy technologies through productive uses. leads to missed opportunities in targeting investments and interventions where they are most needed.
  • Economic Underdevelopment: Energy is a critical driver of economic activity, particularly in rural and underserved regions. Lack of access to reliable and affordable energy sources constraints businesses, agricultural productivity, and even educational opportunities, perpetuating cycles of poverty.
  • Social Inequality: Disparities in energy access contribute to broader social inequalities. Communities without adequate energy access face disadvantages in health, education, and economic potential, widening the gap between different societal groups.

The goal of this project is to enhance the Energy Access Explorer (EAE) tool by integrating additional datasets on both the supply (such as detailed mapping of solar home system penetration) and demand sides (identifying potential areas for productive uses of energy) of energy access in Kenya. By doing so, the project aims to provide stakeholders with more precise, actionable data to support targeted energy solution implementations that foster socio-economic growth, particularly in underserved and rural areas. This enhanced tool will help stakeholders more effectively plan renewable energy projects, and assess their impact on productivity in key sectors like agriculture, thus contributing to more sustainable and equitable development outcomes. 

The project goals

The primary goal of this project is to enhance the Energy Access Explorer (EAE) by integrating additional datasets on both the supply and demand sides of energy access in Kenya. This enhancement will include detailed mapping of solar home system penetration and identifying areas for productive uses of energy, particularly in agriculture, to support socio-economic growth. The project will unfold over the following planned phases:

  • Data Collection and Integration: The first phase involves collecting and integrating granular data on solar home systems at the building level across Kenya. This step is critical for creating a comprehensive map that highlights the current state of solar energy penetration and identifies gaps in energy access.
  • Assessment and Mapping for Productive Use: In this phase, the project will assess and map areas for productive uses of energy, focusing on agricultural activities. This will help in identifying regions where energy access can directly enhance productivity and economic outcomes.
  • Algorithm Development: Development of algorithms to predict energy demand and supply at a granular level will follow. These algorithms will be crucial in forecasting future energy needs and planning for expansions in energy infrastructure.
  • Open Sourcing and Documentation: Ensuring that the developed algorithms are open-sourced and well-documented is essential for replicability in other regions, such as Uganda and India. This phase will focus on creating clear, accessible documentation and resources that can be used by other developers and researchers.
  • Pilot Implementation and Nationwide Scale-Up Planning: The final phase involves piloting the implementation in Makueni County, with an eye towards planning for a nationwide scale-up based on the insights and results from the pilot.

Thus, this project aims to deliver a significantly enhanced version of the EAE that not only provides detailed insights into current energy access but also aids in planning and implementing effective renewable energy solutions across Kenya. By providing a more comprehensive, data-informed view of energy needs and opportunities, this initiative is expected to catalyze targeted investments in renewable energy, thereby boosting agricultural productivity, supporting economic growth, and contributing to sustainable development.

**More details will be shared with the designated team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

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Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus



Skill requirements

Good English

Machine Learning Engineer

Experience working with Machine Learning, and/or Data Analysis is a plus.



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