AI Energy Access Analytics

The Problem

“Quantifying and Targeting Energy Access Gaps Faster”

Organizations face these key challenges:

Fragmented, low-quality data across utilities, census agencies, telecoms, and satellite sources makes access metrics inconsistent and difficult to trust

High cost and long lead times for surveys and site assessments delay investment decisions and reduce responsiveness to population and demand shifts

Mis-targeted electrification projects lead to low utilization, poor revenue recovery, stranded mini-grids, and persistent reliability issues

Impact When Solved

High-resolution access and reliability maps (e.g., 250m–1km grids) updated monthly instead of every 2-5 yearsOptimized grid vs. mini-grid vs. standalone solar recommendations reducing levelized cost of supply by 5-15% in targeted regionsEarlier identification of high-loss/high-outage feeders and underserved settlements enabling 10-25% reliability improvement and 2-5 pp loss reduction

Real-World Use Cases

Smart Grid Management and Optimization

A smart grid is like upgrading from an old landline to a modern smartphone for your electricity network. Instead of just pushing power one way from big plants to homes, the grid becomes two‑way, with sensors and software that can see what’s happening in real time, shift loads, use home batteries and solar panels, and prevent or shorten outages.

Time-SeriesEmerging Standard

9.0

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.