Data wrangling to provide solar energy access across Africa
Data wrangling to provide solar energy access across Africa


More than 600 million people in Sub-Saharan Africa have no access to electricity, and the majority of those have no documented financial history. These two facts set the stage for some incredibly cool applications of data science. A family can light their home and keep necessary electronics (such as a cell phone) charged using a small solar panel and battery, but most solar devices are not affordable to a vast number of people making $2 a day or less.

One solution to this problem is offering solar energy kits on a Pay As You Go basis, providing financial loans to families until they are able to pay off the cost of their device (paying around 10-20 cents per day over several months to years). However, people with severely restricted income are very susceptible to financial shocks and oftentimes exhibit sporadic payment behavior which poses an interesting prediction problem. By mining data from a variety of data sources - demographic, past repayment patterns, weather and climate data, satellite imagery, and data from the devices themselves - we can predict repayment and develop credit histories for solar energy users. This rich and unique dataset can be used to develop credit profiles for individuals, allowing them access to credit for other life-changing loans or utilities.

In addition to financial information, the solar devices themselves send millions of bits of information (from their internal temperature, to the amount of energy flowing from the panel, to the number of hours of light that the kit is providing) regularly using a GSM chip. We can identify, diagnose, and predict system malfunction using anomaly detection and classification algorithms, and even plan mobile clinic routes to fix the systems in the field. Information transferred through GSM, along with the financial data amassed through loan repayment, provide a fascinating dataset on which to model and explore. Data analysis and machine learning techniques allow increased energy access to those for whom the costs of solar were previously prohibitive, as well as increased adoption of renewable energy sources in a rapidly growing population.


Brianna leads the data science team at Fenix International. Their work spans multiple countries, including the US, Uganda, Zambia, and Ivory Coast. She and the data team at Fenix work on a wide range of problems to help provide clean, safe, and sustainable energy to people living off the grid in Sub-Saharan Africa. She has a bachelor's degree in Physics from Johns Hopkins University, a master's degree in Physics from the University of Wisconsin - Madison, and a Ph.D. in Neuroscience from the University of Wisconsin - Madison. After years of particle physics and functional MRI analyses, she took a break from academia and served as a Peace Corps volunteer in Northern Uganda. She's delighted to use her background in big data at the perfect crossroads of sustainable energy and energy access for underserved populations.

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