Oluwadamilare Aluko is a microgrid project developer at Gommyr Power Networks, where he engages stakeholders and manages various microgrid solutions. His work focuses on project scene set-up, conceptualisation, and development of renewable-powered microgrids such as eStreet and other decentralized low carbon energy technologies. He also handles offer preparation & business development for projects across Africa and the United Kingdom. Oluwadamilare uses machine learning and advanced spatial geoprocessing techniques to extract renewable microgrid intelligence from satellite imagery.
Before joining Gommyr Power Networks, he worked in research and development for metering & control technologies, solar PV design & engineering, and project management. Oluwadamilare holds a BSc in Physics and an MSc in Satellite Applications from the University of Strathclyde.
Gommyr Power is a project developer, independent advisor and engineering company focused on microgrid, energy storage and localised renewable energy systems. Gommyr focuses on an integrated approach using reliable local power as the base case for a wide range of additional services, productive uses and growth opportunities.
Space in Africa caught up with Oluwadamilare to discuss how he uses satellite imagery in Gommyr’s quest to reduce energy poverty in Africa. The following Question and Answer session ensued.
Are your microgrid projects related to your work in Africa?
Very much so. Although Gommyr is physically located in the United Kingdom and Greece, many of our active markets are actually in Africa. All the projects I work on are in Africa – Nigeria, the Democratic Republic of Congo, Tanzania, Mozambique and so on. I live here (referring to the United Kingdom), but my work is mostly in Africa.
Could you explain how your work is related to Africa? Are you extending microgrid projects to Africa?
Yes, at Gommyr, we develop renewable microgrid projects in African areas where there is energy poverty. For us, the way to accelerate renewable microgrid uptake is through the utilisation of intelligence derived from geospatial analysis and satellite imagery.
Can you explain how you use Geospatial information and satellite imagery to further your renewable energy projects in Africa?
Of course! Basically, you can break it down into two phases. Phase one is the pure identification phase – using mostly medium resolution imagery from satellites – to identify off-grid clusters or settlements of people without energy access. This phase also includes identifying the important features which would be relevant for a specific project. Features such as distance to the city, distance to water, population – using number of buildings as a first proxy, etc., can be extracted using medium satellite imagery and machine learning.
Interestingly, at Gommyr, we take it a bit further by analysing high-resolution imagery to get more insights than just the aforementioned. I am referring to satellite imagery in the order of at least 40cm resolution by high-resolution imagery. We use it to get a further understanding of the socioeconomic distribution of a project region. In the renewable microgrid space, before developing projects for off and weak grid regions, it is imperative to fully understand that region in terms of demography, economy, purchasing power, etc. This is essentially what we do.
For one of our projects in the Democratic Republic of Congo, using high-resolution imagery – if I remember correctly, that imagery had an effective resolution of 41cm due to the inclination angle of the satellite when the imagery was captured – we were able to count over 33,000 buildings in a particular project region. We were also able to pinpoint the commercial buildings in the city with the use of proxies.
We used a building size and building type proxy to identify the commercial buildings and expanded this across the whole region using machine learning. This is usually important to most renewable microgrid developers because you want to have your projects as close as possible to productive use and commercialisation – since they consume more electricity.
With satellite imagery analysis, we created a density distribution of commercialisation across the whole city. Depending on the specific nature of the project, like the above, satellite imagery analysis helps us determine the best possible location for our projects. We use geospatial analysis to mark the ground areas and their sizes for the projects. In addition to commercialisation and “residentialisation”, this information is some of the insights that inform project location and characteristics.
We also use satellite imagery analysis to assess the economic purchasing power of our project regions, again with the use of proxies. In some cases, the proxies could be static cars in compounds. We assume that areas with cars generally have a higher economic purchasing power than areas without them. It helps us determine the areas with people who are more likely to pay for energy. For high-accuracy analysis of this kind of workflow, you probably need about 15cm resolution imagery – which can be easily obtained from a drone. However, one can still derive good results from 30-40cm of well-processed satellite imagery. These are just a few ways we integrate satellite imagery analysis and machine learning into our workflows – with the ultimate goal of reducing Africa’s energy poverty.
Are the satellites and drones owned by the company you work for?
No, we are purely a renewable energy microgrid engineering company. There are a lot of commercial satellite imagery aggregators and distributors from whom we procure imagery from.
Do you contract African satellite imagery distributors for your imagery?
It isn’t easy. There aren’t many African high-resolution satellite true colour imagery distributors. For the resolution of imagery we work with and the remoteness of the regions we develop projects in, even European and American satellite data aggregators (who own and operate most of the true colour Earth Observation satellite constellations in space) don’t usually have enough high-quality data. I mean, the market is governed by demand and supply. In the absence of high demand for data from a particular region, most constellation operators and data aggregators will not spend many of their resources processing imagery from such regions.
Do you think the development of the African commercial downstream sector will alleviate this problem?
Yes. I mean, the best set of people to get quality data for Africa will be Africans themselves. We know our region best and thus will provide the best data that can aid in solving most of the continent’s challenges. The industry must invest in downstream capacity building collectively.
What challenges do you face in carrying out your job?
As I earlier mentioned, it is difficult to obtain high resolution high-quality recent imagery of remote areas in Africa, primarily due to a lack of demand for imagery of such regions. Also, there is a price uplift for getting imagery of regions in certain countries. This means that there could be as much as an 80% increase in price when tasking a satellite for imagery of regions in these countries.
Regarding the first challenge, is it an issue of Africa? Or merely an issue of remoteness?
It’s a bit of both. The remoteness is definitely a factor. Remote regions are usually of less interest for Earth Observation true colour satellite imagery aggregators. However, another factor is the ownership of the Earth observation constellations acquiring the data. For example, if a satellite constellation is owned and/or operated by the Nigerian national space agency, it would naturally be of more interest to capture and process satellite imagery of various country regions – including the very remote regions.
What are your thoughts about the future of space in Africa?
It is interesting. Recently I’ve come across three or four African companies doing a lot concerning satellite imagery analysis. However, the whole continent needs to invest collectively to ensure more benefits for Africa. There is a reason why the European Space Agency exists even though many European countries spend millions of dollars developing their own national space programmes. If you look across the continent (Africa), most countries experience similar challenges; energy poverty, political unrest, connectivity, floods, etc. You look at the 17 SDGs, and there’s probably not one SDG that space cannot contribute to. For me, it is clear that the future of African space is a collective one and a point of serious investment.