Residential energy efficiency

With energy poverty affecting 50 million EU households, COSMIC aims to provide residents with smart tools and decision-making support to reduce energy costs and optimise energy use. Smart HVAC systems and heat pump management will play a key role in achieving energy savings.

Pilots are real-world testbeds where Core technologies and AI innovations are combined to develop and validate scalable energy solutions.

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Private housing in multi-family buildings

Pilot locations in Belgium

This pilot empowers residents and trustees with detailed energy diagnostics and microservices across four buildings – 160 smart-monitored apartments generating over 1,100 data points per hour – to support solution clustering and replication.

Core technologies:

Building‑Energy Web Platform

AI solutions from Third Parties:

An AI assistant that answers user queries, provides predictive insights and guide users.
AI-based OCR for energy meters data based on photos to improve operational efficiency.
Mechanisms to increase user engagement through for example behavioral analytics, gamified energy challenges and personalized goals.

Problem addressed: The energy management process in residential and multi-unit smart buildings is facing two challenges: (1) the manual, error-prone commissioning of energy meters after building construction, and (2) the lack of engaging and effective tools for residents to interact with their energy consumption data.

Expected outcomes: Enhanced energy awareness and behaviour change among residents through a scalable and replicable smart energy management model for residential buildings. Increased operational efficiency for smart meters identification and initial calibration.

Image from pexels.com by Kindel Media

Renewable energy community

Pilot location in Portugal

This pilot empowers a renewable energy community in Porto to personalise energy services, improve accessibility and optimise resource management. by enhancing appliance interoperability and using Generative AI – including large language models.

Core technologies:

Home Energy Management System (HEMS)

AI solutions from Third Parties:

Monitoring of assets and back-end integration with HEMS existing features
Integration of Generative AI features with User interfaces
Replicating the HEMS ensuring interoperability with large range of appliances, enabling seamless data acquisition for AI model development
Develop energy management control strategies at household level based on generative AI interpretability from real time data acquisition.

Problem addressed: Complexity of energy optimization in a renewable energy community due to the fluctuation of the energy demands and unpredictable energy generation by the renewables

Expected outcomes: Enhanced user engagement and accessibility, Improved appliance interoperability and data integration

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Efficient usage of heat pumps

Multiple locations

This pilot aims to optimise heat pump energy usage across Europe by using AI and data-driven insights to deliver personalised recommendations that improve efficiency, user comfort and adaptability to regional climate and energy market variations.

Core technologies:

Energy‑Grid Optimisation Microservices

AI solutions from Third Parties:

Data-powered consumer research
AI-enhanced front-end application

Problem addressed: Inefficient operation of heat pumps due to the lack of real-time performance insights

Expected outcomes: Optimizing energy use of heat pumps via AI by profiling usage, forecasting demand, and offering recommendations based on weather and tariffs. Aims to improve energy efficiency and comfort across different EU climates.