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EINSTEIN Project Overview

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Presentation from the EINSTEIN project seminar.

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EINSTEIN Project Overview

  1. 1. EINSTEIN Project Overview <Stakeholder Feedback> Daniel Coakley, Marie Curie Research Fellow Integrated Environmental Solutions Ltd. EINSTE
  2. 2. RESEARCH AND DEVELOPMENT
  3. 3. OPERATION ‘Model and data-driven performance improvement’
  4. 4. EINSTEIN Project • EINSTEIN (FP7, 2014-2017) Simulation Enhanced Integrated Systems for Model-based Intelligent Control(s) – Development of automated prediction, optimization and fault detection algorithms for integration with the IES software for building performance optimization • Funding: 4-Year EU-funded Marie Curie IAPP – Jan. 2014 – Dec. 2017 • Partners: IES and TCD • Topics – Fault Detection – Prediction – Optimisation – Overall system integration
  5. 5. IES-VE & SCAN / ERGON SCAN – Cloud-based data acquisition, analysis and visualisation for Buildings. ERGON - Import, manage and interrogate real building data / schedules and use them inVE simulations. IES-VE – Integrated suite of simulation software for the built environment. • Dynamic thermal models; • Detailed HVAC and Control systems; • Ratings and accreditation (LEED, BREEAM etc.)
  6. 6. Building Information Silos Building Information Model (BIM) • Geometry, • Materials, • Constructions etc. Building Management System (BMS) • Temperature, • CO2 • Set points, • Schedules, • Energy & Water; Occupants • Connected devices (WiFi, Cellular); • Feedback; Rooms • Booking schedules; • Lecture timetables; Weather Station • Wind speed; • Temperature; • Humidity; • Rainfall. Technical documentation • Drawings • Op & Maintenance manuals for equipment etc.
  7. 7. EINSTEIN Platform Overview Optimisation Prediction Calibration Profiles [Ergon] Building Energy Model (WBS / ROM) FDD DSS / MPC / Scenarios
  8. 8. Model Based FDD Knowledge / Rule-based: uses expert user-defined rules (e.g. APAR) which govern system behaviour; Data-driven: uses historical building data, Statistical Methods, Empirical Data, Machine Learning; Model-based; uses a calibrated detailed system model
  9. 9. Prediction / Optimisation • Multi-objective control optimisation; – Comfort; – Economics; – Carbon, Etc.; • Integrates – Historic/predicted weather conditions; – Building & system thermal response; – Occupant schedules & feedback; – Economics (i.e. electricity / gas tariff);
  10. 10. Overall Integration Building Data IES-SCAN <ERGON> Modelling / Prediction Fault Detection / Optimisation Intervention (DSS / MPC)
  11. 11. Operational WBS Models • Increasing prevalence of BIM and energy modelling at building design phase; • Compliance and rating systems (e.g. LEED) starting to recognise operational performance, rather than traditional design performance; • Advantage of operation models for FDD / MPC; – Can be used to effectively monitor and diagnose discrepancies between design intent & operational performance (i.e. performance gap); – Adaptable to changes in building or system operation (compared to solely data-driven approaches); – Capable of simulating different control scenarios, recognising actual system response; – Allows optimisation of control strategies using real performance feedback
  12. 12. Thank you! Daniel Coakley Email: daniel.coakley@iesve.com Web: www.iesve.com

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