Electricity
Load forecasts using separate models for domestic, commercial and industrial consumption, ranging from quarter-hourly to annual sales.
Load forecasts for electricity, gas and district heating
Load and sales forecasts based on historical consumption data, weather, the calendar and customer profiles. Automated, covering both the short and long term, and providing a reliable basis for procurement, scheduling and the balancing group.
Influencing factors
Factors that go into a good load forecast
Consumption follows overlapping patterns: weather, day of the week, season, and customer profile. mP Xnergy automatically takes these factors into account and weights them individually for each metering point.
- Historical consumption and load profiles
- Weather data such as temperature, solar radiation and wind
- Calendar information such as public holidays and school holidays
- Seasonal patterns and daily profiles
- Customer-specific profiles; for industrial customers, this also includes production schedules
Divisions
Dedicated models for each sector
Electricity, gas and heating are affected by different factors. That is why mP Xnergy does not use a one-size-fits-all model, but calibrates each sector individually.
Gas
Temperature-adjusted demand forecasts that reliably reflect weather conditions and seasonal effects.
District heating
Forecasts based on degree-day figures, outdoor temperature and operating hours for generation and storage control.
Here’s how it works
From measurement data to the timetable, with no manual work involved
Connect data
Measurement readings, weather data and calendar data are automatically imported.
Select a model
mP Optimizer finds the most appropriate model for each metering point.
Calculating forecasts
Short-, medium- and long-term, on a fixed schedule or event-driven.
Submit result
Via an interface to SAP, retail and timetable reporting systems.