AI Prosumer Energy Optimization

Helps prosumers optimize self-consumption, export, and storage behavior using price signals, forecasts, and device-level control.

The Problem

“Optimize prosumer energy use amid volatility”

Organizations face these key challenges:

High uncertainty in net load due to weather-driven PV and variable customer behavior, leading to costly imbalance and poor dispatch decisions

Fragmented asset control (PV inverter, battery, EV, HVAC) and limited real-time telemetry, making coordinated optimization difficult at scale

Misaligned incentives and complex tariffs (dynamic pricing, export caps, demand charges) that customers cannot practically optimize manually

Impact When Solved

10-25% reduction in customer energy bills via automated tariff- and forecast-aware scheduling10-20% peak demand reduction in targeted feeders, improving reliability and deferring capex upgrades10-30% reduction in imbalance costs through improved short-term net-load forecasting and coordinated DER dispatch

The Shift

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

AI-Driven Energy Flexibility Optimization

This is like giving the power grid a smart autopilot that learns when to turn power plants, batteries, and big industrial loads up or down so you always have enough electricity at the lowest cost, with fewer blackouts and lower emissions.