Predict consumption before it happens
Machine learning models that analyze history, weather, and events to project energy demand, so you buy and generate the right amount, cutting cost and waste.
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less waste and cost with more accurate demand forecasting (industry benchmark)
Getting demand forecasting wrong is expensive
Buying and generating energy without seeing future demand leads to waste and avoidable cost. The gaps we see most in the sector:
Traditional forecasting errs and creates waste
Simple statistical methods don't keep up with real consumption variation. Energy is bought or generated in excess, and every error becomes a cost no one recovers.
Complex patterns no one captures
Consumption depends on seasonality, habit, holidays, and load behavior that all intersect. Spreadsheets and fixed rules can't see that combination, and the forecast stays blind to what matters.
Dispatch decided on guesswork
Without a reliable number for future demand, dispatch planning becomes a gamble. Extra generation gets dispatched for safety or too little out of optimism, and both cost dearly.
Forecast error hits the bottom line
Buying energy in the wrong market, dispatching unnecessary thermal generation, or sitting exposed to peak prices: every forecasting inaccuracy converts into direct operating cost.
From history to dispatch, demand under control
A real, deployable capability for the energy sector: forecasting models that turn data into buying, generation, and dispatch decisions.
Models that learn from history, weather, and events
We train machine learning models that combine consumption history, weather forecasts, and event calendars to project demand with far more fidelity than the historical average. It's a real, applicable capability for the sector.
Dispatch planning based on numbers, not hunches
The forecast feeds dispatch planning: when and how much to generate or buy in each window. The operation starts deciding on a projected, reliable curve, not a last-minute estimate.
Balancing load against generation
With future demand estimated, you can balance load and generation across the day, reduce unnecessary dispatch, and buy the right amount. Less waste, more predictable cost.
A forecasting dashboard to see ahead
A dashboard consolidates the demand forecast, the scenarios, and the deviations from actuals, so the team can track, adjust, and build confidence in the model over time.
Technologies & partners
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Node.js
AWSPythonTensorFlowNode.jsAWSCommon questions about demand forecasting
Let's forecast your energy demand with precision
Bring your forecasting and dispatch challenge. You'll leave the conversation with a clear technical path, from data preparation to the model in production.