Tackling Energy Poverty in Nigeria Through Artificial Intelligence

Tackling Energy Poverty in Nigeria Through Artificial Intelligence

Can AI help to address energy poverty in Nigeria where more than 100m people lack stable access to electricity?


By Laura Clark Murray 


A staggering 1 billion people on Earth live in energy poverty

Without stable access to electricity, families can’t light their homes or cook their food. Hospitals and schools can’t dependably serve their communities. Businesses can’t stay open.

Energy poverty shapes and constrains nearly every aspect of life for those who are trapped in it. As the Global Commission to End Energy Poverty puts it, “we cannot end poverty without ending energy poverty.” In fact, energy poverty is considered to be one of humanity’s greatest challenges of this century.

In Nigeria, Africa’s most populous country, more than half of the 191 million citizens live in energy poverty. And though governments have been talking for years about extending national electricity grids to deliver energy to more people, they’ve made little progress.


With such a vast problem, what can be done?

Rather than focusing on the national electricity grid, Nigerian non-profit Renewable Africa 365, or RA365, is taking a different approach. RA365 is working with local governments to install mini solar power substations, known as renewable energy microgrids. Each microgrid can deliver electricity to serve small communities of 4,000 people. In this way, RA365 aims to address Nigerian energy poverty community-by-community with solar installations.

To be effective, RA365 needs to convince local policymakers of the potential impact of a microgrid in their community. For help they turned to Omdena. Omdena is a global platform where AI experts and data scientists from diverse backgrounds collaborate to build AI-based solutions to real-world problems. You can learn more here about Omdena’s innovative approach to building AI solutions through global collaboration.


Which communities need solar microgrids the most?

Omdena pulled together a global team of AI experts and data scientists. Working collaboratively from remote locations around the globe, the team set about identifying the regions in Nigeria where the energy poverty crisis is most dire and where solar power is likely to be effective. 

To determine which regions don’t have access to electricity, our team looked to satellite imagery for the areas of the country that go completely dark at night. Of those locations, they prioritized communities with large populations that incorporate schools and hospitals. Also the collaborators looked at the distance of those communities from the existing national electricity grid. In reality, if a community is physically far from the existing grid, it’s unlikely to be hooked up anytime soon. In this way, by analyzing the satellite data with population data, the team identified the communities most in crisis.

In any machine learning project, the quality and quantity of relevant data is critical. However, unlike projects done in the lab, the ideal data to solve a real-world problem rarely exists.  In this case, available data on the Nigerian population was incomplete and inaccurate. There wasn’t data on access to the national electricity grid. Furthermore, the satellite data couldn’t be relied upon. Given this, the team had to get creative. You can read how our team addressed these data roadblocks in this article from collaborator Simon Mackenizie. 


What’s the impact?

The team built an AI system that identifies regional clusters in Nigeria where renewable energy microgrids are both most viable and likely to have high impact on the community. In addition, an interactive map acts as an interface to the system.

AI in Nigeria

Heatmap with most suitable spots for solar panel installments

RA365 now has the tools it needs to guide local policymakers towards data-driven decisions about solar power installation. What’s more, they’re sharing the project data with Nigeria Renewable Energy Agency, a major funding source for rural electrification projects across Nigeria. 

With this two-month challenge, the Omdena team delivered one of the first real-world machine learning solutions to be deployed in Nigeria. Importantly, our collaborators from around the globe join the growing community of technologists working to solve Nigeria’s toughest issues with AI.

Ademola Eric Adewumi, Founder of Renewable Africa 365, shares his experience working with the Omdena collaborators here. Says Adewumi, “We want to say that Omdena has changed the face of philanthropy by its support in helping people suffering from electrical energy poverty. With this great humanitarian help, RA365 hopes to make its mission a reality, bringing renewable energy to Africa.”


About Omdena

Building AI through global collaboration

Omdena is a global platform where changemakers build ethical and inclusive AI solutions to real-world problems through collaboration.

Learn more about the power of Collaborative AI.

AI for the People: How to Stop the Nigerian Energy Crisis

AI for the People: How to Stop the Nigerian Energy Crisis

How an African NGO hosted an Omdena AI challenge to bring solar energy to Nigeria through data science and machine learning.


By Omdena Partner Ademola Eric Adewumi


I am the Co-Founder of the NGO Renewable Africa and in the following I share my experiences regarding our first Omdena Challenge. Within the two month AI challenge, Omdena’s team built a powerful tool to identify the most suitable spots for solar panels.



Africa is rich in renewable energy sources, especially solar energy.

Unfortunately, the abundance of solar power has not been harnessed yet.

In my home country Nigeria, most people grew up in darkness with regular power outage. Many businesses nowadays use diesel-powered generators to produce electricity. I went through several periods without electricity altogether.

A change needs to happen.

That is why, Omachonu Joshua Dominic and I founded the NGO RA365, with the mission of providing renewable energy to most at-need communities in Nigeria to stop the energy crisis.


A welder doing his daily work with a diesel powered generator

Diesel engine supplied electricity


Non functional Electric Transformer

Non Functional Transformer


Quickly, we realized the potential of using AI for solar energy but were looking for a partner to build the solutions.

Supported by Sebastian Laverde who joined Omdena’s community early on we got in contact with Omdena and within two weeks designed an AI challenge on their platform.

The goal was to end energy poverty in Nigeria via building machine learning algorithms suitable to identify the best spots for solar power.

We were impressed and encouraged by the attendance of willing participants brought together from all around the world by the large network of Omdena.

At this point, Dominic and I knew we had the right minds to revolutionize the electrification system in Nigeria.

The community of AI experts swung immediately into action and the members displayed a penchant enthusiasm. The sacrifice and empathy to participate in a mission of helping a country they had never seen was overwhelming for the RA365 team.

It provided us a deep dive into the power of data science and machine learning

Much thanks to Rudradeb Mitra, an astute personality with a heart of gold, for being a blessing to this generation. Through his humanitarian assistance, and overseeing leadership, he has brought great minds together.

To all, we have to say,

We are very grateful.


The challenge was separated into two phases — the research phase which was executed by the Omdena community. This involved using machine-learning based heatmaps and grid coverage analysis to identify regional clusters in Nigeria that are most viable for installing solar panel stations.


Heatmaps that show Solar Irradiance

The developed heatmaps are reproducible in other African countries or developing places where solar energy could bring in value.

If you are interested in a more technical understanding of the AI solutions, check this article.

The second phase entails the physical deployment of a mini-off-grid solar power generation substation to power a remote community.


Solar Farms

The enthusiasm exhibited by the Omdena community sparked reassuring confidence in the RA365 team to deliver on its part of the challenge.

Our team had made great strides in using the state of the art results obtained from the challenge to built significant partnerships in the solar energy industry in Germany and with Nigerian governmental agencies.

The greatest hurdle for the second phase of the challenge is raising funds to deploy the mini-off-grid substations. At the moment, we are seeking funders for a mini-off-grid substation, which is estimated to be above 300,000 Euro.

This can provide electricity to approximately 4000 inhabitance for a span of 20 years.

Lastly, we want to say that Omdena has changed the face of philanthropy by its support in helping people suffering from electrical energy poverty.

With this great humanitarian help, Renewable Africa hopes to make its mission a reality, bringing abundant energy to Africa.

About Omdena

Building AI through global collaboration

Omdena is a global platform where changemakers build ethical and inclusive AI solutions to real-world problems through collaboration.

Learn more about the power of Collaborative AI.

AI Applied: Providing Communities in Nigeria With Solar Energy

AI Applied: Providing Communities in Nigeria With Solar Energy

Energy grid analysis and AI to identify sites that are most suitable for solar panel installation across Nigeria.


By Omdena Collaborator Simon Mackenzie


Giving access to renewable energy in places most at need has the potential to solve many of the most pressing problems in today’s world.


The UN has identified a number of goals to make the world a better place.


The power of building AI through collaboration

Imagine if we could focus our AI efforts to help solve the biggest problems in the world — Poverty, Hunger, Health, Education.

As you are reading this you are probably interested in AI. Perhaps like myself you have completed courses in machine learning; put inordinate effort into a Kaggle project to reach the top 50; made hobby projects to generate twitter posts in the style of Trump or to tell the difference between an elephant and a giraffe. Perhaps you have a career in data science targeting advertising or building a chatbot for customer service.

And perhaps like me, you find these intellectually stimulating but that something is missing.

Surely there is a way to use AI to make a more positive impact on the world?

I recently joined an Omdena Challenge with a multi-national team to do just that. In the two-month AI challenge, hosted with Nigeria based NGO Renewable Africa, we focused on providing affordable, clean energy, which is a fundamental need in developing countries.


The problem: 100m people without electricity

Nigeria has a population of 200m people yet only half have access to electricity. Without electricity, there are no computers or the internet. There are no fridges to keep food fresh. There is no electric water pump. There is nowhere to charge a mobile phone.

Schools and Hospitals struggle to provide basic services. Widening electricity access is an essential first step for improving education, healthcare, and local economies.

Centralized planning for electricity in Nigeria has failed. The government has built-in failure by fixing prices and profits; there is widespread corruption, and banks will not lend to new power plants. Meanwhile, half the existing plants lie idle and the rest operate below capacity.

Millions live under the grid but not connected to it; previously connected but some equipment failed and has not been replaced, or they have electricity but only enough for a light bulb; or they have it but it is unreliable due to daily power cuts.

Can AI help to make a difference in Nigeria?


The solution


Off-grid, solar energy puts local people in control


One solution to this problem is localized, off-grid, renewable energy in the form of solar panels servicing small communities of up to 4,000 people. This protects against any single point of failure; puts the power in the hands of local people; and is more easily financed as it requires less capital and has faster returns.

The first step in implementing this is to prioritize where to put the panels. It would take 25K+ panels to provide a minimal electricity supply to everyone in Nigeria. We can apply data science to ensure the available funding is used efficiently to provide electricity to as many people as possible.


AI in Nigeria: Overcoming challenges

A major part of the challenge is to find the data; validate it, and label it. There is no shortage of data but what is out there was not always accurate or fit for our purpose.

Other challenges were to develop skills in analyzing geographic sources that were at varying resolutions and to find ways to present these in a simple way.

Nobody on the project had strong prior experience in these areas so it was a learning experience. Modeling was less of an issue for this project because there were many existing models that we could adapt to. Some of the data issues are described below.


Identifying the demand

Nigeria can be split into 775 census areas but it is a huge country so a single census area can be 1000km2. In that area, we need to know if the people are all in one town or scattered across hundreds of villages. The typical way to do this is to use satellite images to classify land use then allocate the census numbers to buildings by applying estimates of relative density. This can be enhanced with random forest to incorporate a multitude of other variables.

It sounds great in theory but in practice, the numbers just look wrong.

Furthermore, there is a question mark over the accuracy of the census. This is used by the central government to allocate funding so there is some incentive for local officials to boost the numbers. We can’t even rely on the total population being 200m in total for the same reason! At best this is an estimate and at the local level could be completely wrong.


The solution

A recent healthcare project found that none of these models worked on the ground. So they created a new, bottom-up, statistical model based on areas where they knew the population; then expanded this via micro-surveys.

This looks much more realistic.


Where to supply the electricity?


Satellite images show the existing electricity. The grid has been mapped using machine learning.


To find target sites we need to exclude those that already have electricity. In addition, those close to the grid were given low priority as they are more likely to receive it directly in the future. The volume of available and free satellite data is incredible. In particular there are night-time light images that clearly show towns that have light.

But how do we validate that? Here we can leverage the magic of google maps. I find it awesome to be able to zoom in on a road in Nigeria to see whether it has street lamps. Based on a selection of test towns it was possible to calibrate and validate the data from satellite images.

For the electricity grid, you may think the government and electricity companies would know where their cables are. But they don’t!

Fortunately, we could leverage an existing model to identify electricity cables that used a combination of machine learning on satellite images and human checking.



Our outputs


AI in Nigeria

Clusters of 4,000 people without electricity and more than 15km from the grid


Cluster analysis was applied to the population data to identify groups of 4,000+ people within a small radius filtering out those that already
had electricity or were close to the existing grid.

These candidate clusters were then combined with other data such as a solar irradiance model, health and education establishments. This produced a map showing potential sites together with a spreadsheet ranking the opportunities.

The map + technical explanation can be found here.




The sponsoring NGO Renewable Africa is now able to confidently survey a selection of sites that are suitable for solar panels. In addition, they are sharing the data with the Nigeria Renewable Energy Agency (REA). The REA is a major funding source for off-grid, rural electrification projects in Nigeria. The data collected will allow much better targeting especially outside major towns where government data is lacking.

More refined targeting will enable many more people to get electricity per $ of investment.

This means better healthcare, education and economies; and will potentially improve the quality of life for millions of people.

For the project team, this was an amazing opportunity to learn more about Nigeria, AI, renewable energy, satellite imagery, population modeling, and
technical skills in mapping and analyzing geographic data.


Most satisfying was using AI to solve a real problem affecting real people. AI is not about models but about solving problems.


About Omdena

Building AI through global collaboration

Omdena is a global platform where changemakers build ethical and inclusive AI solutions to real-world problems through collaboration.

Learn more about the power of Collaborative AI.