SolutionAI for power grid operators
An AI assistant for grid operators and power planners. It reads live grid load and weather every few minutes, forecasts demand 48 hours ahead with a likely range, checks every forecast against what actually happened, and explains the numbers in plain words. It supports decisions and never controls the grid.
The grid console on a saved day of real load data. The live demo further down is the same console.
The context
Grid teams work from live load, weather feeds and the previous day forecast. The AI opportunity is to forecast further ahead, show how good the forecast really is, and explain it in words a planner can use.
Demand can fall to well under half of peak on a quiet night and set a record on the hottest day. Heat, humidity, holidays and festivals all move it.
The grid team must know tomorrow's demand to buy enough power and keep spare capacity for the peak. Too little means power cuts, too much means paying for power nobody uses.
Temperature and humidity move cooling load, so a good forecast reads the weather as well as the load.
Load sits on the operator's website and weather elsewhere. Turning them into a forecast and an explanation takes time a control room does not have.
General background, not engineering or trading advice. Specific systems, data feeds and volumes will come from the operator during discovery.
What is the current issue?
The issues under each area are working hypotheses to test with grid teams, not findings.
Load every 5 minutes and weather every hour, in one place
AI opportunity. The load every 5 minutes and the weather every hour are read and saved automatically, with the time received.
See the live grid screenThe next 48 hours, hour by hour, with a likely range
AI opportunity. A machine-learning model forecasts the next 48 hours with a likely range, and locks a day-ahead forecast every night.
See the briefingEvery forecast scored against the real load
AI opportunity. Every forecast is checked against the real load and scored against two simple guesses, on past test days and on real days since it went live.
See the accuracy screenAsk why, and get the reasons in words
AI opportunity. An AI assistant explains the numbers in plain words and runs what-if scenarios. Every number comes from the data or the model, never from the AI.
See the assistant answersHow can AI help?
Each workflow starts from work grid teams already do. Data pipelines and a forecasting model make every number, an AI agent explains it, and a named person decides.
The operator starts from a live picture instead of copying numbers.
Grid load is read every 5 minutes, by itself.
Temperature and humidity are read every hour.
Missing readings and odd values are marked before anyone relies on them.
Sees the latest reading next to the forecast and the likely range.
Every reading is saved with the time it was received, so any forecast can be scored later.
Tools the assistant calls
Illustrative function names for a pilot design.
Today
Open the operator site and a weather site and copy the numbers by hand.
With AI
Load and weather arrive on their own, saved with the time received.
The intelligence layer
The four steps share one data layer and one forecasting model, so each new question reuses what is already built. Data flows up, and scoring and logging wrap every step.
Live load and weather, saved with the time received
A model forecasts, scoring checks it
AI explains numbers fetched by tools, a person decides
One console for the grid team
What would the user see?
This is the grid console on a saved day of real load data: the briefing, the live grid, accuracy and an AI panel. Ask a suggested question and read an answer with its chart and numbers.
Live data · grid operator
The console watches the grid every 5 minutes, forecasts demand 48 hours ahead and explains it in plain words. Decision support only: it never controls the grid.
How much power the grid was using at the latest reading, 17:30.
MW (megawatts) measures power used at one moment.The latest forecast expects 4,927–5,169 MW for 17:00–18:00.
The range is where demand is most likely to fall.The busiest hour of the day. Likely range: 4,991–5,206 MW.
Day-ahead forecast made at 00:05 by the forecast model.The grid's current load is 5,190 MW as of 17:30. The day-ahead forecast puts today's peak at 5,053 MW around 18:00, while the latest forecast for tomorrow's peak is 5,176 MW. Yesterday the forecast was off by 10.7% on average, better than a simple guess that repeats last week (27.3% off).
Written from the readings available then. Numbers come from the forecast tools, checked against the data.The AI explains the forecast in plain words. Every number it uses comes from the live data or the forecast model.
Or ask
Watch live demand against the forecast and see when it runs high.
Plan tomorrow's purchase and the evening peak from a forecast with a range.
See how accurate the model is and where it misses.
Get the story behind the numbers in plain words, on demand.
Key screens
Each tab opens the same console on one page and works on its own. The load and weather are a saved day of real data, and the answers are pre-written demonstrations.
Read the briefing, then ask the AI to explain today's forecast.
Live data · grid operator
The console watches the grid every 5 minutes, forecasts demand 48 hours ahead and explains it in plain words. Decision support only: it never controls the grid.
How much power the grid was using at the latest reading, 17:30.
MW (megawatts) measures power used at one moment.The latest forecast expects 4,927–5,169 MW for 17:00–18:00.
The range is where demand is most likely to fall.The busiest hour of the day. Likely range: 4,991–5,206 MW.
Day-ahead forecast made at 00:05 by the forecast model.The grid's current load is 5,190 MW as of 17:30. The day-ahead forecast puts today's peak at 5,053 MW around 18:00, while the latest forecast for tomorrow's peak is 5,176 MW. Yesterday the forecast was off by 10.7% on average, better than a simple guess that repeats last week (27.3% off).
Written from the readings available then. Numbers come from the forecast tools, checked against the data.The AI explains the forecast in plain words. Every number it uses comes from the live data or the forecast model.
Or ask
Current demand, how it compares with the forecast, the expected peak and a short summary written from the numbers.
What comes next?
This page is the concept and working demo. Pilot scope, data, budget and timeline are agreed only after the grid team has seen it.
The scope areas this solution covers.
This page, with a working console on a saved day of data.
Adjust as more information arrives.
Validate the problem, data and needs.
Scope, data, integrations, criteria, budget, timeline.
Show the idea working, so stakeholders react to something concrete.
Output: a stakeholder-ready demo.
One grid area and an agreed set of real data feeds, alongside today's planning routine.
Output: a scoped pilot with agreed measures.
More grid areas and questions on the same platform, run and supported.
Output: a supported service.
Built for grid operations
A grid team will ask how good the forecast is, where each number came from and what the AI can touch. The design answers that from the start.
The console replays a saved day of real load data and pre-written answers. A working system reads the live grid every 5 minutes.
It forecasts and explains. It never controls the grid or issues an instruction.
Machine learning makes every number. The AI only explains numbers fetched from the data and the model.
The accuracy page compares the model with simple guesses on past days the model never saw.
Review the demo with your team.Tell us what a pilot must prove.
The load and weather on this page are a saved day of real data and the answers are pre-written demonstrations, not engineering or trading advice. This page is not an official system of any grid operator.
