BEE
The BEE - Building Energy Efficiency – service optimizes building energy consumption. By easily integrating into a building and combining its data with external sources, BEE automatically derives an optimal control to minimize energy usage and shift consumption to renewables. The service is carried out by Eeneman Oy, a smart energy company, Unetiq GmbH, an agency for Artificial Intelligence and Metropolia University of Applied Sciences.
C.in-City
C-in.City is the CO2 emission monitoring solution of the future. It provides cities with a near real-time live carbon emission monitoring solution, articulated around 5 services that empower all stakeholders with actionable insights regarding the emissions in all sectors: Automated Reporting; Carbon Budget Tracker; Simulation tool/What-if; Climate Decision Targeting; and MRV for Climate Finance. The solution is relying on a core database of near real-time emission data at the city level, which is the main innovation. This is made possible by the ability of the consortium to leverage never used before datasets with AI, and then translate this information into emission figures. C.in-City is created by a consortium of Kayrros, La Javaness and NEXQT.
Enerbrain
Enerbrain's Plug & Play IoT solution achieves energy efficiency, carbon footprint reduction and indoor comfort in buildings thanks to an advanced AI logic. The solution smartly interconnects all energy stakeholders and controls any type of building in an automated and optimized manner, keeping energy flows in balance. Enerbrain offers an all-in-one platform, implementing the current concept of “Energy as a Service”. SPIKE could manage energy flows in single buildings and within entire urban districts, interconnecting residential and non-residential buildings.
Heroes
The solution that Heroes proposes aims at reducing energy consumption of public buildings by using AI, IoT & Cloud technologies. By reducing energy consumption, they directly reduce CO2 emissions through the reduced consumption of electricity and/or natural gas. The solution will use data in a much larger & more diverse scale than solutions currently on the market, as we aim to use building data that is available through all participating cities. We’ll gather this data into a cloud platform, which makes it possible to compare buildings and scale by easily “adding” new buildings to the database. In addition, using Cloud technologies will help to preserve data security and privacy.
Holoni
The vision of the HOLONI project, a consortium of the Norwegian AlphaVenturi and the Danish Energinet is to provide smart city ecosystems an intelligent and decentralised peer-to-peer energy marketplace platform enabling the secure and traceable real time exchange and trading of green energy between peer participants within a given virtual cluster. It contributes to empower end-consumers and prosumers in driving CO2 emission reduction through a more sustainable and efficient energy use.
IBM Danmark
IBM Danmark's Flex Planner Tool dramatically reduces the time to assess, plan and promote investments for energy flexibility and efficiency in buildings. The AI tool is based on an ecosystem of data that leverages information from public data and learning from previous experiences, ensuring accuracy and establishing the best preparation for grid-interactive efficient buildings.
Loopfront
Loopfront is an AI collaboration platform made to empower building owners and the construction industry, removing barriers and creating a structure for circular activities. All materials are surveyed and their data tracked, making reports on waste, emissions, and financial savings available at all times.
Rebase
The consortium of Rebase Energy and Grid Singularity has developed an open source platform for setting up local energy markets and smart agents for energy management and trading. The solution empowers energy producers to take part in the energy transition and handle the complexity of distributed energy assets such as solar, electric vehicles and batteries.
Symvio
The Symvio solution helps building owners and operators increase the efficiency of building systems, reduce CO2 emissions with energy savings of 10% and CO2 saving potentials up to 20%. The solution supports operators in enhancing maintenance processes to increase tenant comfort with fault free operations. Symvio uses automated data analytics and machine learning methods to supervise building systems such as heating, ventilation and air conditioning systems.
The Predictive Company
The Predictive Company's solution is a predictive energy management system supported by AI, that learns the energy profile of a building infrastructure, to forecast its real energy demand with the end of an autonomous and optimized operation of the HVAC systems. It goes beyond the state of the art from two reasons:
Firstly, it is based on modelling the behaviour of the Coefficient of Performance of the equipment, therefore is centred in optimization of the asset’s operation and its maintenance perspective. Secondly, the datasets of different buildings can be shared to impact over the efficiency of each single facility. This approach requires management of sophisticated AI tools and data management procedures that are integrated in the proposed solutions after years of research and experiments.
