The project focuses on Machine Learning and Artificial Intelligence. It develops models and algorithms that use big data to improve energy efficiency, increase comfort & flexibility, and reduce maintenance costs.
B4B ambition: offering (future) solutions for the most important challenges of building management
- Energy waste due to improper operation and defective components
- Energy wasted due to mismatch with user preferences
- Integration of building in renewable E-infrastructure (smart grids)
- Data integration for multiple platforms
- Validated integrated prototypes of software plug-ins for (1) smart monitoring and control of buildings and installations, resulting in 20-30% less energy consumption and lower maintenance costs, and (2) increasing controllable energy flexibility in buildings, applied to multi-resource buildings.
- Validated prototypes of data-driven and user-centric interfaces that contribute to user comfort, health and well-being.
- Standardized Smart Readiness Indicator (SRI) and quick-scan.
- Methods, guidelines and standards for data integration for smart building infrastructure, with associated open data platform
- Learning community to share/exchange knowledge about self-learning software for smart buildings and the link with smart grids
Research collaborations around buildings that will be used (open) living labs, valididation or use cases in five integrated work packages
- WP 1: Self-diagnosing installations for energy efficiency and smart maintenance
- WP 2: Intelligent energy flexibility control strategies
- WP 3: Smart user centred interfaces and feedback
- WP 4: Data integration
- WP 5: Learning communities
Brains 4 Buildings
Led by TU Delft, the project consortium is made up of 39 organizations including knowledge institutions, installation companies, energy consultancies, platform/interface developers, building owners and managers, technology suppliers, industry associations and other subject matter experts.
More information: Mirjam Harmelink M.G.M.Harmelink@tudelft.nl