NODA Heat Network – optimisation of heat networks
A district heating system is demand-driven by nature. However, the network operator can traditionally only control the supply, and then only in a static way. NODA Heat Network connects the energy chain and provides the network operator with a tool for dynamic control of both supply and demand. Additionally, the NODA system can be used by the network operator to provide active energy services to their customers as well as a platform for predictive maintenance.
NODA Heat Network is based on patented technology and provides solutions for network optimisation, including dynamic supply temperature, demand side management, network station control, active energy services and predictive maintenance. The NODA system integrates seamlessly with existing IT-systems and can co-exist with almost any current data management system. For demand side management and energy service integration the NODA system is compatible with most existing controllers.
There is no lock-in mechanism with NODA. The system can be configured and expanded as needed, and will adapt to the requirements of every specific district heating network.
Dynamic Supply Temperature
Setting the supply temperature is the primary way for a network operator to control the supply into their network. Traditionally this is done by using static settings, which relate the supply temperature to the current outdoor temperature.
Dynamic Supply Temperature provides a way to dynamically control the supply temperature. The system uses data-driven analysis and self-learning models to continuously relate the current supply temperature to the actual operational requirements throughout the network.
By actively controlling the supply temperature it is possible to substantially increase the efficiency of the production, for example relating to CHP optimisation and decreased distribution losses.
Demand Side Management
Normally a network operator has no ability to control the demand itself, even though the whole system is by its very nature demand-driven. Any traditional optimisation effort is forced to simply following the demand.
Demand Side Management provides a tool for network operators to control the demand while ensuring quality of service. By controlling the demand in a demand-driven system, it is possible to truly start optimising the operational behaviour of that system.
By controlling the demand it is possible to reduce expensive fossil peaks, balance base loads, synchronise demand with market or marginal production prices and actively reduce return temperatures.
Data Driven Analytics
Many network operators are collecting measurement data from heat meters in their networks. Normally, this data is primarily used for billing purposes. However, many network operators want to further leverage this data and use it for more in-depth analysis. The problem is that most existing data management systems do not have the functionality for such large scale analytics.
Data Driven Analytics is a platform for advanced AI-based analytics, and integrates freely with most data management systems using standard API solutions, and can co-exist “under the bonnet” with existing solutions.
Data Driven Analytics transform data into information and knowledge, which can then be used for predictive maintenance by the network operator themselves or used to create targeted consumer communication content.