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Using Data to Bring Light to the Last Mile

photo of man walking through gate of a solar mini-grid plant
A Tara Urja solar mini-grid installation, Derni, Bihar, India.

Some of the most exciting energy innovations today, from pendulum power to gravity lights, are being implemented down isolated dirt roads or across remote grasslands to communities living along the “last mile” of electricity delivery.

That mile, though, presents a challenge: how do would-be providers calculate the power needs of first-time consumers often living at or below the poverty level who need electricity to improve their economic status? The stakes are high. Overestimate, and a government or private company wastes precious resources; underestimate and the provider may lose disillusioned customers unwilling to pay for insufficient or unreliable electricity.

Electric machines and lights lift communities out of economic poverty as well as energy poverty by helping to create sustainable jobs, which will be even more important in a post-corona virus world. Indeed, economists predict global economic instability could hit emerging markets especially hard. Modern electricity access is essential for the resilience of poor and low-income communities to weather global crises like pandemics and economic shocks like recessions.

But to encourage providers to invest, innovation needs to extend not just to creating power generators, but to analyzing energy needs. So The Rockefeller Foundation has earmarked $3.8 million over three years to collaborate with energy-sector data scientists to develop a leading-edge algorithm that can predict future electricity demand among low-income customers.

New Times, New Solutions

The traditional approach to electrification has remained unchanged for more than a century; a centralized model in which large-scale electrical generation has been combined with the relatively slow build-out of expensive transmission and distribution lines across nations.

Historically, the rollout of national grids prioritized cities and towns along with commercial zones and corridors, reaching more remote areas only once there was sufficient revenue to subsidize them. For many economies and over decades, this approach worked, enabling most of North and South America, Europe and many parts of Asia to achieve universal electrification.

Countries still lacking universal access are now among the poorest in the world, which means they have less money to spend on electrification when it arrives—at a time when reliable energy has become increasingly central to modern economic activity and a key tool for poverty reduction.

Extending national grids would cost $2000 or more per connection for hundreds of thousands of communities around the world, making microgrids and other mixes of energy solutions a relative bargain. But still, the initial capital investment is high, so providers and others need to have an accurate sense of consumer needs. ESMAP 2019, Mini-Grids for Half a Billion People: Market Outlook and Handbook for Decision Makers.

Once public utilities and private providers realized the costly errors they were making in calculating anticipating electrical usage, they strategized that the best way to find out how much energy is enough was simply to ask. But door-to-door surveys consistently proved unreliable.

“People are not the best predictors of their own consumption,” says Ashvin Dayal, senior vice president of the Foundation’s Power Initiative. “Ask me how much bread I’m going to consume for the next month—I can’t tell you. So how are you going to predict how much you will use of something you’ve never had?”

What providers found, in fact, is that consumers over-estimated by four times the amount of electricity they would use if it were available, Dayal says. As the baseball-playing philosopher, Yogi Berra is famously quoted as saying: “It’s tough to make predictions, especially about the future.”

Making Use of Machine Learning

Enter the Electricity Growth and Use in Developing Economics (e-GUIDE) Initiative, which is aimed at teaching machines to make accurate predictions using historic consumption data and satellite imagery. In essence, known electricity consumption patterns are overlaid with new consumers’ assets, settlement patterns and access to local infrastructure. This information then develops predictions about particular communities’ future use, and those predictions are tested.

e-GUIDE is part of the Foundation’s focus on working with partners in support of UN Sustainable Goal 7, which calls for everyone to have access to affordable, reliable, and modern energy sources by 2030. But efforts to expand the reach of microgrids or other smaller-scale generators cannot occur in a silo. “If you can’t measure it, you can’t manage it,” says UMass Amherst’s researcher Dr. Jay Taneja, a man with a passion for energy data whose father and grandfathers were all electrical engineers.

The capacity to accurately predict energy demand will give providers, governments, and investors the ability to determine the best-fit methods of distributing energy and the confidence to make financially viable and sustainable decisions.


  • If you can’t measure it, you can’t manage it.
    Dr. Jay Taneja
    Assistant Professor, Electrical and Computer Engineering, UMass Amherst

The e-GUIDE work, led by Taneja, also includes researchers at Columbia University, Carnegie Mellon University, the Rochester Institute of Technology, and Colorado School of Mines. As a further extension of the partnership model, these researchers have strong connections to various utility leaders and off-grid companies in Africa.

By late May they intend to begin beta-testing an openly available application programming interface (API) that will enable data and insights on electricity consumption growth to flow across borders and throughout the sector. They hope it can be test-driven in two or three countries by year’s end. The API development is a key part of the Foundation’s ten-year priority to enable upward economic mobility for 200 million people by accelerating access to and consumption of reliable electricity in underserved, low-income communities.

The API development is a key part of the foundation’s ten-year priority to enable upward economic mobility for 200 million people by accelerating access to and consumption of reliable electricity in underserved, low-income communities.

The goals are lofty, but it’s a step-by-step process rooted in details. “We want to learn from past patterns set elsewhere,” Taneja says, “and at the same time, we have to look at what is specific about different environments. We’re learning from places where we have good data, like Kenya. Then the real value will be to gain insights into places where no data are available. The goal is to provide data resources that can help improve the efficiency of investment and the planning processes for the sector.”

e-GUIDE researchers are currently working with utilities in Kenya and Uganda, and discussing future work in Ethiopia, Rwanda, and Nigeria, and are open to working with utilities elsewhere.

Developing Human Capital to Support Machine Learning

Good predictive data is a key part of a larger puzzle that can help officials decide whether other interventions, including access to commercial appliances, finance, and markets, are needed to stimulate the use of electricity and help people move out of poverty. Another part of that puzzle calls for developing critical human capital. So the e-GUIDE project supports two programs for improving data and analytics capacity in the electricity sector, one that provides year-long fellowships and the other that offers internships.

The fellowship program, run at the African Leadership University, supports five utility professionals each year by helping them, largely remotely, learn ways to use data in their work. Two are from Kenya and the others are from Uganda, Nigeria, and Rwanda.

The internship program places students from the master’s program at CMU’s Africa campus in Kigali, Rwanda, at various electricity companies all around the African continent. Last summer was the first year, and 12 students participated; a few ended up being hired full-time by the company where they were interning, Taneja says.

“If you have great data about a place, does that mean planning will just improve? No,” Taneja says. “So it’s also about providing the human capital to make use of those data.”

e-Guide Blueprint:

  1. Create a collaboration with leading data scientists in the energy field who have between them close connections with utilities and governments in the areas of high global energy poverty. This is important to gain access to information essential for building tools that can accurately predict energy demand in emerging markets across sub-Saharan Africa and South Asia.
  2. Those data scientists construct measurement and data analytics techniques that are scalable, transnational and verified, using real data on electricity consumption and infrastructure. For example, data from rural Kenya could be used as a base on which to build models for other rural areas with levels of high energy poverty.
  3. The data scientists overlay this known usage data with information about new consumer’s assets, settlement patterns and access to local infrastructure.
  4. We partner with electricity service companies to develop our techniques further, deploy them at scale and build capacity for data and analytics in the electricity sector.
  5. Additionally, we make sure to institute programs such as internships and fellowships to develop human capital in parallel with good predictive data.