The electricity scene from above: Exploring power grid inconsistencies using satellite data in Accra, Ghana

Complicated systems are complicated to monitor. The electric grid is one of the most complicated systems, and subsequently goes under-monitored in many regions around the world that cannot easily afford expensive meters. However, the electric grid is also critical for sustaining a high quality of life, and requires better monitoring than is often available to ensure consistent service is provided. Past work has shown that images taken by satellites during the night, capturing nighttime illumination (“nightlights”), could provide a proxy measurement of grid performance for minimal cost. We build upon earlier work by identifying the pixel z-score – a statistical measurement of a pixel’s illumination relative to its history – as a key method for detecting electricity outages from the often-noisy nightlights dataset. We then train and validate our approach against observations from a network of on-the-ground power outage sensors in our observation area of Accra, Ghana, a dataset representing the largest collection of utility-independent electricity reliability measurements on the African continent. Using multiple machine learning techniques for estimating potential outages from nightlight images, we obtain high performance for predicting outages in Accra at scales as small as a single pixel (0.2 km2) and with training datasets as small as three months of illumination/sensor data. We further validate our methodology beyond the spatio-temporal coverage of the on-the-ground sensor deployment against a human-labeled dataset of outages by neighborhood throughout Accra. Delving deeper into the applications and limitations of available datasets and our work, we conclude by highlighting questions about the generality of our method vital to understanding its potential for low-cost worldwide measurements of grid reliability. •High-resolution measurements of power supply inconsistencies using satellite data.•Z-score of a region’s luminosity is indicative of the region’s power supply inconsistencies.•Logistic regression exhibits high performance in detecting outages using nighttime lights satellite data.