AI Energy Optimization Platform

Provides a cloud-based AI layer to ingest real-time operational data and continuously optimize energy use across assets and sites.

The Problem

“Real-time energy optimization across volatile supply and demand”

Organizations face these key challenges:

Forecast errors and limited probabilistic visibility drive imbalance penalties, over-procurement, and reliability risk during weather events

Siloed control systems for generation, storage, and DERs prevent coordinated dispatch, increasing curtailment and peak demand charges

Operators lack fast scenario analysis and constraint-aware recommendations, leading to conservative decisions, higher O&M costs, and slower response to market volatility

Impact When Solved

Improve day-ahead/intraday load and renewable forecast accuracy by 10–30%, reducing imbalance exposure and reserve procurementLower total portfolio operating cost by 2–5% through optimized dispatch, bidding, and storage cycling under grid constraintsIncrease renewable utilization by 5–15% by minimizing curtailment and aligning storage/DER flexibility with congestion and price signals

Real-World Use Cases

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.

Time-SeriesEmerging Standard

8.5

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.