AI Off-Grid Solar Sizing
The Problem
“Accurate off-grid solar sizing amid uncertain loads”
Organizations face these key challenges:
Sparse or inaccurate customer load data leads to chronic under- or over-sizing and unpredictable performance
High variability in solar resource, temperature, and seasonal demand is not captured in deterministic spreadsheet sizing
Manual sizing is slow, inconsistent across engineers/contractors, and difficult to audit for financiers and regulators
Impact When Solved
The Shift
Real-World Use Cases
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.
Data-driven optimal configuration of hybrid energy storage in park micro-energy grids
This is like designing the right mix and size of batteries for an industrial or campus-sized “mini power grid” so it can quickly ramp power up and down when needed, without overpaying for equipment or risking reliability.