How can SUPERSENSE be utilised to create a real-time snapshot of a district heating plant?

Improving production and emission efficiency will certainly affect all industrial companies. In our previous blog posts, we have addressed why real-time snapshots and data, in general, are key to achieving these goals in the energy industry and district heating production.

We have successfully implemented such solutions for several companies in the energy industry and now want to tell you step by step how a real-time snapshot is formed and what the trendy term ‘IoT’ means in this context.

Defining district heating network and plants

The very first step is to define all the plants and installations covered by heat production and distribution. At its simplest, this covers the power plant as well as the distribution network. Often, however, a district heating company has at least one smaller backup power plant in addition to the main plant. Larger companies can, of course, have more main power plants as well as numerous backup power plants. However, it is essential to record accurate information on all of these.

It is essential to know, for example, whether the plants have common or separate control rooms, whether they have integrated automation systems and whether they are interconnected at all.

Designing the physical solution 

The next step is to define the concrete means by which data will be collected into one place, i.e. how IoT solutions will be implemented in practice. This is determined by the automation system of the district heating plant (whether it is a Citec, WinCC or Siemens system for example) and how the data is extracted from this system. Or, if there is no control room technology in use, and the power plant operates under local control, a solution must be designed to transfer the data forward.

On the other hand, if some of the backup plants are not connected to the same system, the required devices must be installed in order to make the data available from these as well. Possible future expansions and how new plants will be integrated with the solution will also be taken into account.

Designing the software solution 

Once all data sources have been connected to the network with IoT solutions, the data collected by them must be routed to something. With SUPERSENSE, that place is Microsoft’s Azure cloud service, and the data is owned by the district heating company itself. SUPERSENSE processes data from a variety of sources into a format that is useful to the business.

It is essential that data is collected and processed continuously so that the user can observe everything that is happening in the power plants and the district heating network in real-time, at both the macro level and in more detail at the micro-level if desired.

In addition, the software application can utilise machine learning solutions that enable, for example, forecasting of future production needs based on historical information. The accuracy of this can be further improved by combining production data with other information such as weather data and forecasts.

Presentation of data

The last step is to present the data in a clear format. This can mean, for example, setting up Dashboard views for people in different roles. From these dashboards, individuals can see real-time metrics that help them optimise the power plant’s production and emissions efficiency. Dashboards are implemented with Microsoft Power BI, allowing users to create new views whenever they want.

In addition, if forecasts are utilised, they can also be presented via the same Dashboard, thus further enhancing production optimisation.

In addition, the data can be conveniently compiled into reports using the same solution.

Prerequisites for future district heating production

This will enable the energy company to take concrete measures to improve both production and emission efficiency. As real-time data is gathered, production and emission efficiency will only improve further.

In addition, the real-time Dashboard can be used to avoid and resolve problem situations by using it as a basis in the service control system of service personnel. Other means of utilisation can be easily found for the data.

A real-time snapshot is a basic prerequisite for improving efficiency, so it makes sense to start your efforts sooner rather than later.