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Energy efficiency

Predictive Energy Optimization delivering 30% energy reduction on average.

4. May 2023

Solution provider


With market-proven services, such as Predictive Energy Optimization, ReMoni ensures on average a 30% energy reduction in non-residential buildings.

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Energy efficiency and resource optimization are challenges in existing buildings where the necessary data is not available or where it is expensive and time-consuming to integrate with e.g., the Building Management System to make it work more intelligently.

In this case ReMoni added a new type of intelligence to the existing buildings through the use of clamp-on sensors, AI algorithms, and control hardware.

The world’s buildings account for around 40% of the total energy consumption and around 36% of the world’s CO2 emission (EU data) – the majority of this is when the buildings are in use.

This calls for massive action to reduce both the energy usage and CO2 emission. When talking about energy efficiency, most still think about renovating the buildings, however this is a costly and time-consuming task often associated with risk.

The potential in exploiting the existing systems in the buildings without making large investments in new systems or installations is huge.

To reach the Sustainable Development Goals and become more resource cautious, we need to speed up the progress on becoming more energy efficient.


At a large Danish municipality, ReMoni deployed the service Predictive Heating Optimization in the winter of 2022 in several buildings.

The service, Predictive Heating Optimization, is basically intelligent control of the heating system in the building. Here ReMoni installed our unique clamp-on intelligent solution, which monitors the water and heating consumption in the building, including the usage of the building.

This is then sent to ReMoni’s cloud Microservices, which combines it with data on weather forecast and other relevant data. ReMoni’s AI algorithms builds a digital model of the building ensuring a continuously updated model of the thermal characteristics of the building, heating needs etc.

The ReMoni algorithms determines when heating is needed in the municipality’s buildings and controls the heating systems based on this.

This Danish municipality has a Building Management System installed already; however, it was deemed necessary to find a solution without integration requirements and a possibility for fast results.


At the Danish municipality actual measured savings are in the range from 25,3% to 34,9% on the heating consumption, meaning that the savings have been correlated with the meter data from the building – and the indoor comfort temperature has not been impacted or changed.

The buildings vary in thermal characteristics and year of construction/renovation, however the measured savings are around 30% in all cases. This shows that it can be worthwhile to both renovate a building to optimize thermal characteristics but on the same time ensure efficient usage of the existing systems and installations.

The concrete customer has chosen a Risk-Free payment model, where the service is paid by giving a cut of the saving to ReMoni – but the municipality has not paid anything in advance, no start-up cost or any other operational/running costs. This means that the payback time is instant.

As part of becoming more energy efficient, the CO2 emissions is also reduced. It has not been possible to calculate the specific CO2 reduction in this case (yet), due to a lack of data. However, it can be assumed that the CO2 reduction follows approximately the same reduction trend as energy consumption meaning that it is in the range from 25-35 % CO2 reduction.

This results in value being creating for not only the municipality who reduced heating consumption by 30% but also for the environment with the reduced footprint.