Intelligence to Heating

ma 10. syyskuuta 2018 12.00.00

Welcome to our renewed webpage! We have been working on them for a while so that you can learn more about Fourdeg’s unique technology to optimize buildings’ heating consumption and thermal comfort.


Did you know that a quarter of a residential building’s maintenance charge occurs from heating?(1)

Or that the individual thermal sensation varies from person to person by 6°C?(2)

Fourdeg starts to have a regularly appearing blog about all major topics concerning the business case:

  • Energy efficiency in buildings
  • Load balancing
  • Consumer comfort
  • Smart and wireless technologies

The topics will reflect both state-of-the-art, viewpoints on the business, and play around with future prospects. The writers change by the topic of expertize.


So, what does intelligence in the heating system mean?

Intelligence can be referred through the data analysis ladder. In Fourdeg, we want to know exactly, how each room in a building is heated. The concept can be also connected to data analytics. The first step is to notice, what has happened in the building. We are getting an answer through a descriptive data set. On the next step we want to know, why it has happened, so the diagnostic part. At this point, actions do not have real value.

Let’s take an example with a cold winter day.

1. Descriptive: What happened?

The room’s air temperature felt cold in the morning.

2. Diagnostic: Why did it happen?

The room is in the corner of the building and the outdoor temperature was in the night -15 °C. During night drops the room’s indoor temperature decreases too much and the heating system has not enough power to heat it up again in the morning.

3. Predictive: What will happen?

The upcoming night will be as cold as the night before and the room’s indoor temperature will be too low again tomorrow morning.

4. Prescriptive: How can we influence it?

The indoor temperature of the corner room should not be decreased at all. It is more convenient to sustain day-time indoor air temperatures than decrease them. Hence, the user is satisfied in the morning.


We are doing this by collecting different data streams. In this point, the numbers do not have real value. As the data has been organized and presented in a systematic fashion to clarify the underlying meaning, it receives the status of information.

Eventually, by combining different data streams, we learn something new, and can turn that into wisdom.


Want to know more about Fourdeg’s technology on learning indoor environment? Check out our Fourdeg Smart Heating® service and leave your email to receive our brochure.


Sonja Salo

The author is Energy Systems Architect in Fourdeg.