# Energy demand & power-load forecasting - sktime use case

Forecast electricity demand from historical consumption patterns with sktime, and get uncertainty ranges instead of a single number so you size reserves to real risk.

Canonical page: https://www.sktime.net/use-cases/energy-forecasting/

## 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.

## Trust Signals

- 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.

## A forecast without a range is a guess with a bill attached.

- 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.
- 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.
- 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.

## 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. (€0)
- Manual forecasting hours: One model spans the fleet, so analysts stop rebuilding forecasts by hand. (freed)

Illustrative, industry-typical ranges. Your real numbers come from a short scoping call on your own data.

## 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.

## Three steps from your data to a forecast you trust.

- Scoping call: We map your series, your drivers, and where the load forecast hurts most today.
- Pilot on your data: We forecast a real slice of your demand, with ranges, and you see the lift first-hand.
- Run in operations: Roll it into your dispatch and planning cadence, with the maintainers a message away.

## 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.

## 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.