Over-provisioning
Reserve you pay for and never use
Buffer against the worst case everywhere and you hold spare capacity, spinning reserve, and peaking contracts you rarely need.
Use case · Energy demand & power load
Forecast the load,and know the range.
Power demand follows daily, weekly, and seasonal rhythms. sktime learns those patterns from your historical consumption and forecasts them forward with uncertainty ranges, so you cover the evening peak without paying to over-provision every hour.
Power demand forecast · tomorrow, MW
Night · 00–06720 – 840780
Morning · 06–121,150 – 1,3601,240
Midday · 12–181,090 – 1,3001,180
Evening · 18–24Peak1,380 – 1,7001,520
Ranges size reserves to real uncertainty. You cover the evening peak without over-provisioning every hour.
A single number hides the risk. Plan to it and you either buy costly reserve or get caught short at peak.
Illustrative figures.
- Open source
- Free to run, fully auditable, and no per-site or per-meter license.
- Proven methods
- The same probabilistic and global forecasting methods used across demand and load problems.
- Built by the maintainers
- You talk to the people who actually build sktime, not a reseller.
What the status quo costs
A forecast without a range is a guess with a bill attached.
Peak misses
Caught short when it matters
Under-forecast the peak and you cover the gap on the spot market at the worst prices, or pay imbalance and penalty charges.
Manual work
Analyst days lost to spreadsheets
Teams rebuild load forecasts by hand for every site and season, and still have no honest measure of how uncertain they are.
Where the money comes from
Ranges you can trust pay for themselves.
When you know the range, you plan reserves to real risk instead of gut feel. Most of the savings land in capacity you stop holding, the rest in penalties and hours you stop spending.
Less spare capacity & reserve
Probabilistic forecasts size reserve to real risk, not a blanket safety margin.
Fewer imbalance & penalty charges
Forecast the peak honestly, so you settle closer to schedule.
Better trading & peak decisions
Ranges turn buy, store, and shed calls into informed bets, not guesses.
Forecasting license fees
sktime is open source. No per-site or per-meter vendor bill.
Manual forecasting hours
One model spans the fleet, so analysts stop rebuilding forecasts by hand.
Illustrative, industry-typical ranges. Your real numbers come from a short scoping call on your own data.
What you can build on
Built for real load planning.
Probabilistic forecasts
Intervals, quantiles, variance, and distributions through predict_interval and predict_quantiles.
Global & panel models
One model trained across many meters, feeders, or sites at once.
Seasonality & drivers
Hour-of-day, weekday, holidays, and weather as calendar and exogenous features.
Hierarchical reconciliation
Keep site, region, and system-level load forecasts consistent with each other.
How we start
Three steps from your data to a forecast you trust.
- 01
Scoping call
We map your series, your drivers, and where the load forecast hurts most today.
- 02
Pilot on your data
We forecast a real slice of your demand, with ranges, and you see the lift first-hand.
- 03
Run in operations
Roll it into your dispatch and planning cadence, with the maintainers a message away.
Before you ask
The three things operations leads check first.
- Do we need a data-science team?
- No. We help stand it up on your data, and your operators work with the forecasts, not the code.
- Is open source safe for enterprise?
- sktime is permissively licensed, auditable, and widely used in production. Nothing is locked behind a vendor.
- How fast can we see value?
- A short pilot on a slice of your real load data shows the lift before you commit to anything.
Open source meets enterprise
Bring honest demand forecasts into your operations.
sktime is free and open-source. When you want this running on your own load data, the people behind it can help.