Estimating electricity demand in remote villages

Data-driven demand estimation to identify electrification potential at the level of individual villages.

In this case study, we demonstrate how earth observation and machine learning can offer energy demand prediction in remote villages. The case study in Kenya integrates and compares Village Data Analytics (VIDA) and e-GUIDE. It shows how energy consumption predictions have reached a significant degree of maturity and can help electrification site selection and village-level analysis by providing a thorough assessment of the potential for rural electrification at the scale of the entire country and with the precision of individual villages.

The results presented here employ machine learning techniques to draw insights from satellite imagery and electricity consumption data from grid-connected customers. They show how these technologies enable scalable data-driven energy access planning for government planners, utilities, and off-grid electrification companies.

See the full case study here.

Author: EARSC

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