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Saving energy and decarbonizing residential homes

Kiona and Ericsson

Saving energy and decarbonizing residential homes

Saving energy and decarbonizing residential homes

Learn how Kiona’s Edge AI energy management software powered by connectivity, saved c.17.3 million kWh (an average of 7%) net energy - avoiding 1 kTonne of CO2 emissions.


In a collaborative effort, Ericsson together with Kiona show how connectivity and AI can enable significant savings for 356 residential apartment buildings, with a total space of 1.5 million m2 in Sweden and Finland. The analysis was conducted by The Carbon Trust. 

This study was performed using the ITU- T L.1480 standard and is one of the first to do so. The ITU-T L. 1480 provides a methodology for assessing how the use of information and communication technology (ICT) solutions impacts greenhouse gas (GHG) emissions of other sectors

connected homes

The challenge

Buildings, both the construction and their ongoing operations have been on the radar as high emitters of GHG emissions. The challenge is to understand whether connectivity and digitalization can help support this sector in reducing their impact on the planet.

This case-study is about assessing the effect the implementation of an intelligent heating management solution, Kiona´s Edge’s Artificial Intelligence (AI) Steering Function, powered by wireless connectivity, has on energy consumption and greenhouse gas emissions of residential buildings.

The assessed sample consists of 356 buildings and includes residential buildings in Finland and Sweden, assessed for a period of one year. These markets were selected due to availability of data . All buildings in the case study consistently and exclusively used district heating as their heat source, which is common in the selected markets.



The solution

Kiona has provided AI solutions since 2013. The solutions have since then been refined and developed with the help of building physicists, to convert the energy needed for heat into a simulated outdoor temperature used for heating system steering instead of the real outdoor temperature. Powered by connectivity, the AI steering function measures the indoor building temperature and humidity using sensors. A feedback loop is created which further optimizes the steering.

These functionalities are enabled through Edge Hubs which act as gateways to connect the data transmissions between the building sensors, the network, and the Edge platform. Data is consolidated, analyzed, and communicated on the cloud-based Edge platform. Further correlation of this data with weather forecasts and historic building energy performance data is used to calculate the simulated outdoor temperature. End-users access the platform through devices such as personal computers, tablets or smart phones.

The Edge Ecosystem. Picture courtesy: Kiona.

The Edge Ecosystem. Picture courtesy :Kiona.

The result

This study demonstrated that ICT solutions can have a significant transformative impact on energy use and CO2 emissions. In this case, the AI steering function within Edge contains a mix of data and smart algorithms which constantly improve the energy optimization of individual properties. For the assessed buildings, the heating system with the AI steering function provided a net reduction of energy and GHG emissions.

Average net avoided emissions and average net avoided kWh per m2
Average net avoided emissions and average net avoided kWh per m2