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HeatFor - Heat demand forecasting for district heating

CTCN
Technology:
Country:
Denmark

HeatFor (formerly known as PRESS-Prognosis™) is a software system for forecasting of heat demand in district heating systems. HeatFor™ is a self-learning and self-calibrating system. It is based on machine learning, weather forecasts, historical demand and online measurements in order to automatically produce very accurate heat demand forecasts. 

WHY DO YOU NEED HEATFOR?
An accurate heat demand forecast enables efficient planning and operation of both production and distribution of district heating. Accurate demand forecasts are essential in order to optimize and plan production and utilize heat storage units. District heating network operators can also operate their network more efficiently and reliably by having an accurate heat demand forecast. The results are that security of supply is increased and costs are reduced at the same time. HeatFor™ automatically forecasts heat demand and requires minimal effort from the users.

As an additional environmental benefit, CO2 emissions can also often be reduced by having a more accurate forecast and thereby a more efficient planning and operation of production assets and heat distribution network.

KEY BENEFITS
- Automatically and accurately forecasts heat demand in district heating networks
- Increases security of supply for heat customers
- Easy and inexpensive to install, maintain and operate
- Reliable, stable and high availability with a proven operational track record of 20 years
- Low maintenance with minimal interference and interaction required for the client
- Highly flexible. Can be configured to various network configurations with and without dependencies between the networks

HeatFor is an industry leading solution which requires minimal effort from the client and yet yields superior forecast accuracy when compared to competing technologies.

HOW DOES HEATFOR WORK? 
-HeatFor is a self-learning system based on machine learning. On the basis of measurements from the district heating system, local weather stations and meteorological forecasts, the system is not only able to predict the heating demand but can also automatically and continuously improve its predictions as it is fed more data. Based on input data HeatFor automatically identifies and takes the systematic behavior of heat consumers into account.  This means that HeatFor continuously adapts to the actual situation by continuously monitoring the consumption and automatically adapts to changes, such as:

-Changes in consumer behavior
-Changes in the number of consumers
-Changes in the meteorological models
-Changes in the thermal characteristics of the buildings
-Changes in the district heating network

The self-learning mechanism has the benefit, that it is not necessary to establish and maintain a complex physical description of the heating network, which is often time consuming. HeatFor will identify the impact of such changes by itself and quickly adapt.

Heat demand/load forecasting is complicated by the fact that the dynamics of buildings seriously affect the heat demand on an hourly basis. HeatFor system automatically applies an optimal smoothing effect which solves this issue, such that the physical properties of the underlying energy system are modelled correctly.

HeatFor can be installed locally on customer systems or hosted by ENFOR as a service. It is also possible to get a customized support and maintenance agreement from ENFOR or one of the partner companies.

Self-learning and Self-calibrating algorithms for accurate forecasting of heat demand in district heating systems

Integrates seamlessly with SCADA systems for retrieval of heat production Data

Local weather measurements

Locally optimized weather forecasts

HeatTO for temperature optimization of district heating networks

Web-interface available for configuration and monitoring of system

Data integration interfaces based on FTP, SFTP or Web-services supporting numerous formats and file types (CSV, XML, SOAP, JSON etc.)

Runs on all commons server platforms (Windows, Linux)

Date of release: