An ETL pipeline built on Microsoft Fabric that transforms historical usage data into a monthly demand forecast, alongside a live view of every plant, transformer, and the load each one carries.
“We used to plan next month’s output on a feeling built from last month’s numbers. Now we actually have a forecast built from our own data.”
— Operations Lead, CPower
A power company generates enormous amounts of historical usage data, but data sitting in a system isn’t the same as knowing what’s coming next month. Without a real process to extract that data, clean it up, and turn it into something usable, planning for next month’s power demand came down to experience and rough estimation rather than an actual forecast built from the numbers the company already had. On top of that, the company had to separately keep track of its own infrastructure—which plants and transformers covered which areas, and which of them were already carrying more load than the others—with no single place tying that picture together with the demand side of the business.
“We had years of usage data sitting there, and we still couldn’t answer a simple question: how much power will we actually need next month?”
— Grid Manager, CPower
CPower runs its historical usage data through a proper ETL pipeline—extract, transform, load—built on Microsoft Fabric. Data lands in a data lake, gets refined through queries into something clean and usable, and comes out the other side as charts and graphs that show the actual power demand forecast for the coming month, grounded in the company’s own history rather than a guess. Alongside that, the platform holds the company’s knowledge of its own infrastructure—which power plant or transformer serves which area, and which of them is running under heavier load—so demand forecasting and infrastructure management live in the same system instead of two disconnected ones.
“It’s not a dashboard that just shows us the past. It actually tells us what to expect, and it’s built from data we already had.”
— Operations Lead, CPower
Monthly power demand is no longer a rough estimate—it’s a forecast built directly from the company’s own historical usage data, refined and charted through a real pipeline instead of guessed from experience. That same platform shows which plants and transformers are covering which areas and how much load each one is carrying, so infrastructure decisions and demand planning are informed by the same picture instead of two separate ones. For a company managing power across multiple areas, that combination—a real forecast plus a live view of the infrastructure—is what actually lets planning happen ahead of demand instead of reacting to it.
None of this changes how the power itself gets generated or delivered—it just means the company knows what to expect and what its own network can actually handle before the month arrives.
A power company doesn’t lack historical data—it lacks a reliable way to turn that data into next month’s answer. That gap is what made demand planning feel like a guess even when the numbers to do better already existed.
CPower closes that gap with a real ETL pipeline on Microsoft Fabric, paired with a live view of the plants and transformers actually carrying the load—so the forecast and the infrastructure picture come from the same place.
“We stopped debating whose gut feeling about next month was right. Now we just look at the forecast.”
— Grid Manager, CPower