PEKIVE
Based on a number of representative pilots, we will develop cases and processes for the showcasing of an AI-based HVAC management.
Based on a number of representative pilots, we will develop cases and processes for the showcasing of an AI-based HVAC management.
We will implement a digital twin based approach to indoor climate management, with self-learning software for more efficient, predictive and sustainable control of existing HVAC systems. This will be managed according to CO2 levels and prices on the electricity based production.
We will choose typical buildings with both fossilbased and sustainable heat and cooling production, and preferable with dedicated heat/cooling buffering solutions.
3 existing Danish projects will act as benchmark.
We will implement the solutions and monitor them with the leading energy management information system – Omega EMS - and the results will be published online on a dynamic webpage for inspiration to others.
During the project, we will invite to a number of workshops, targeted towards large building owners, the BMS market and the energy consultancy market. These market players are essential in the aim of reaching our Danish 70% 2030 goals, and will demonstrate how Danish companies are rapidly adapting sustainable and innovative technology to achive our national high ambitions.
The project has demonstrated that it is possible to gain significant energy savings and make large non-residential buildings energy-flexible, by implementing AI-based solutions for predictive energy management.
The representative buildings that participated in the project, showed better indoor climate, lower consumption (up to 26% in a specific month) and a technological ability to interact with dynamic electricity prices and CO2 emissions from electricity production in the grid. This is done by connecting a cloud-based solution to already installed BMS systems with open interfaces.
The representative buildings that participated in the project, showed better indoor climate, lower consumption (up to 26% in a specific month) and a technological ability to interact with dynamic electricity prices and CO2 emissions from electricity production in the grid
To be able to monitor impact of such solution, a simple tool to document outcome and key performance indicators have been developed using simple IoT sensors and energy data available from the cloud or from the specific utility.
Specific barriers for accelerated implementation in larger building portfolios have been identified and listed.
The findings have also been addressed to the market segments through several workshops, involving building owners, building operators and energy consultants, resulting in new solution providers in the market. By having cases that can be used for demonstrations, consultants that knows pros and cons and system integrators willing to implement new technologies, this kind of AI-driven solutions is expected to be implemented by more building owners.
Recordings and key take-aways from workshops is available on the project website www.energifleksiblebygninger.dk
Key figures
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Participants
Partner | Subsidy | Auto financing |
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