Forecast optimization for an Energy Supplier
- 4 days ago
- 2 min read
Challenge
District heating networks across Europe operate far below their potential. Many networks were planned and built at a time when energy costs were low and buildings were poorly insulated. Their operating parameters have remained largely unchanged ever since. Today, heat is produced and supplied based on simple rules, primarily depending on the outside temperature, without taking actual consumption patterns at the household level into account. This leads to overproduction, unnecessary heat loss during transport, and increased CO2 emissions. At the same time, smart meters capable of collecting high-frequency consumption data are increasingly being installed. However, in most cases, this data is used exclusively for annual billing. The analytical potential of this data remains largely untapped — until now.
Approach
In a joint innovation project with Diehl Metering GmbH and utility companies from Germany and Denmark, STAT-UP developed a data-driven prototype for optimizing district heating networks. Approximately 900 wireless energy meters were installed in the network to collect high-frequency data on temperatures, flow rates, and energy consumption. After extensive data cleaning and processing, a statistical forecasting model was developed — combining regression techniques with ARIMA time-series analysis, enriched by weather data and consumption-specific patterns such as daily and weekly seasonality. A separate time-delay model accounts for the physical latency of heat transport through the pipe network. The models were deployed in a fully automated pipeline and made accessible to operators through an interactive web application — ready for day-to-day use.
Impact
The forecasting models deliver reliable results across all consumer types — from individual households to large-scale facilities such as hospitals. Aggregated forecasts reliably captured fluctuations in overall network consumption. Crucially, the models are fully transferable to other district heating networks — regardless of size or topology. The web application provides network operators with an accessible tool for demand-based production planning, with the potential to significantly reduce overproduction, lower operating costs, and cut CO₂ emissions.
