Demand forecasting for a Canadian energy provider

The Challenge

Forecasts were built on historical averages and experience, with no way to account systematically for weather, seasonal shifts, or grid variables. When demand came in higher than expected, the company had to procure energy at short notice and at a premium. When it came in lower, capacity sat idle. The margin for error was shrinking, and the cost of getting it wrong was going up.

Our Approach

A multi-variable forecasting model was built on five years of historical demand data, layered with weather signals, regional economic indicators, and calendar effects. The model was validated against held-out periods before deployment to make sure the accuracy gains were real.

Rolling 7-day and 30-day forecasts were integrated into the operational planning workflow, refreshed daily. Confidence intervals came alongside point estimates so teams could plan for a range rather than argue over a single number.

A monitoring layer tracked how the model was performing over time and triggered retraining automatically when accuracy started to drift.

Key Deliverables

• Multi-variable demand forecasting model with weather, calendar, and economic signals
• Daily-refreshed 7-day and 30-day rolling forecast pipeline
• Confidence interval outputs for risk-adjusted planning
• Forecast accuracy monitoring with automated drift detection and retraining triggers
• Integration with operational planning and procurement workflows

“The shift from gut feel to model-based planning changed how our operations team works every single week.”

— Head of Operations Planning, Canadian Energy Provider

Results

Engagement

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