POLITECNICO DI TORINO

1. POLITECNICO DI TORINO TRIBUTE and DIMMER

2. DIMMER - The context • One of the major challenges in today’s economy concerns the reduction in energy usage and CO2 footprint in existing public buildings and spaces without significant construction works • The concept of Smart City • The main idea is to make an intensive use of ICT to improve efficiency of energy utilization, renewable energy integration and comfort in cities through • Smart grid (electricity/thermal) • Smart mobility • Smart communities • Smart energy systems

3. DIMMER - Challenges • Challenges • Heterogeneous types of buildings • Special attention is paid to historical buildings, which are typically less energy efficient and impose tight deployment constraints to avoid damage by extensive retrofitting • Heterogeneous information systems and data sources • Promote pervasive usage of ICT through new business models • What we need: • Integration technologies • Information sharing • Interactive means (audio / video / AR / 3D virtual models)

4. DIMMER – Smart City Information System • Smart devices • Data collection system • Information repository/models (e.g. BIM) • Decision support system • User awareness, profiling and social behavior analysis

5. DIMMER – From BIM to DIM • Bring information models from building level to district level • Building models • Distribution network models • Real time interaction/visualization • A/R • Q/R codes • Virtual district models

6. Manchester DIM DISTRIC LEVEL BIM BUILDING LEVEL AR PEOPLE LEVEL Turin

7. DIMMER - Outcome • District Information Model and Management for Energy Reduction • Monitor energy consumption, envirnomental parameters and energy production • Actuate energy relevant parameters at buildings (e.g. fan coils, lighting) and district level (water temperature in district heating) • Represent buildings and network in a virtual model with real-time data visualization • Optimize energy efficiency and promote local energy balancing exploting renewable energy • Promote user awareness through A/R • Social behavior analysis

8. Business Model Users EE Engine Algorithm Cost Awareness WEB QRCode BIM Simulation and Visualization DIM Grid Ontology DB - Interoperability Middleware WSN

9. District Heating Data cloud web-based Weatherconditions Smart building Smart building Smart building Data are available by tablet and smartphone in AR using QR Code Smart building Smart building Smart building Smart District ENERGY BALANCING Smart building Smart building Smart building People move from house to work/school and vice versa Sensor node public private

10. TRIBUTE • As of today, building energy performance simulation tends to show large discrepancies with real energy performances during the building lifetime. • Building energy performance simulation tool tend to have difficulties in estimating these performances, while being very efficient during the building design phase. • TRIBUTE aims at proposing a novel approach for monitoring and assessing the building energy performance, at providing a comprehensive energy optimization at building level and a continuous estimation of the building state of health. • It will be based on adaptive and predictive techniques, while taking into account the inhabitant habits and needs. • Relying on this monitoring tool, a continuous energy flow optimization for the building will be proposed, to insure the best usage of available renewable energy sources. Inhabitant requirements and all type of energies will be included in the optimization scheme.

11. TRIBUTE • Based on a wireless sensor networks, building energy consumption will be monitored while external sensors, such as temperature and irradiance sensor will help establishing a prediction of renewable energy production. • The TRIBUTE smart sensor network (temperature, CO2, occupancy, humidity, noise, and thermography) will be the base for the development of a self-adaptive building/occupancy building model that includes the inhabitant’s behaviour.

12. TRIBUTE • Deployed and demonstrated on three validation sites, in different climates and with different building use, the HautBois system will help inhabitants to bring their house in line with European directives and will lead to recommendations for new certification standards. • As a summary, thanks to its adaptive behaviour, HautBois will bring a reliable energy assessment in a reduced time and a building optimization tool, in which inhabitants will play a pivotal role.

13. TRIBUTE - Approach

14. TRIBUTE – PoliTO Role • We can contribute to: • Design of the smart sensor network (make it energy-efficient and autonomous) • Design of middleware and web services for interoperation of heterogeneous sensors • Eventuallycontribute on predictive and adaptive model,especiallyforpredictionofenergyconsumption, exploitingexperience in embeddedsystemenergyestimation

15. TRIBUTE – PoliTO Role • Runtime adaptationof monitoring parameters and sensor network configurationsuch as sample rate, numberofactivesensorsbased on the model needs • This has thepurposeofimprovingenergyefficiency and autonomy ofthewireless sensor nodes, to avoidfrequentbatteryreplacement • Developmentoftechniques to provideunlimited autonomy to wirelessnetworksbased on energyharvesters (e.g. PV cells) • Thiswillincreasetheefficiencyof monitoring and limit management costs • Developmentofmiddleware and web servicesto exposemonitoredparametersindependentlyfrom HW characteristics (interoperability)