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Development of Calibrated Operational
Models for Real-Time Decision Support
and Performance Optimisation
Daniel Coakley BE...
Structure
• Introduction
• Energy in Time Methodology
– Model Development, Performance Analysis & Calibration;
– Control o...
INTRODUCTION
Energy in Time Project
Model Development & Calibration
Control Optimisation
Company Background
• Founded 1994 with HQ in
Glasgow;
• Offices worldwide;
• Focused on delivering
sustainable solutions f...
Energy in Time Overview
Simulation based control for Energy Efficiency
operation and maintenance
• Real building data (BMS...
Model Calibration
 Building energy models may be used in all phases of
BLC from design to commissioning and operation.
Ho...
Prediction / Optimisation
 Prediction algorithms are required in order to
determine future trends over short control time...
METHODOLOGY
Overview
Model Development & Calibration
Control Optimisation
Methodology Overview
• Three phases for project implementation:
– Stage 1: Model Development and Calibration;
– Stage 2: M...
Static Model
Parameters
Model
Profiles
<FFP>
Building
Operational Data
<SCAN>
Base Model <VE>
Sensitivity
Analysis
<Python...
Calibrated Base
Model <VE>
Re-calibrated
Operational Model
Performance
Criteria Met?
YES
Automatic re-
calibration of
Inpu...
Re-calibrated
Operational Model
Model Variant 1
Stage 3: Control Optimisation: In this phase,
we introduce the concept of ...
CASE STUDY: SANOMATALO
Sanomatalo – Active Model
Sensitivity Analysis
Normalised Sensitivity Index
Parameter
Total
Energy
[MWh]
Total
System
Energy
[MWh]
Boilers
Energy
[M...
Measured and Simulated data were compared for
the calibration period for the following output
parameters:
 Heating Coil L...
Calibration – Manual Update
Based on inputs from results visualisation and
sensitivity and performance analysis, the model...
Genetic Optimisation
Genetic Optimisation is used to further refine the
model by automatically modifying static input
para...
Final Measurement & Verification
TABLE 1: FINAL CALIBRATION PERFORMANCE METRICS - SANOMATALO
Performance
Criteria
Mean
Obs...
CONCLUSIONS
Calibration process summary
Conclusions
Future Work
Calibration Process Summary
Calibration Process employs a
number of techniques to improve
model calibration accuracy and
e...
Conclusions
• There are many tools and methods available to aid model development and calibration – lack
of clear guidance...
Future Work
• Complete testing of approach for four EU sites:
– Test Site 1: Airport in Faro, Portugal
– Test Site 2: Offi...
Thank you!
Daniel Coakley BE PhD CEM MIEI MEI
Research Fellow, Integrated Environmental Solutions Ltd.
Adjunct Lecturer, N...
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Development of calibrated operational models of existing buildings for real-time decision support and performance optimisation

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Daniel Coakley's presentation from the recent CIBSE Symposium on “Integration for whole life building performance.”

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Development of calibrated operational models of existing buildings for real-time decision support and performance optimisation

  1. 1. Development of Calibrated Operational Models for Real-Time Decision Support and Performance Optimisation Daniel Coakley BE PhD CEM MIEI MEI Research Fellow, Integrated Environmental Solutions Ltd. Adjunct Lecturer, National University of Ireland Galway Secretary, ASHRAE Ireland CIBSE Technical Symposium 2016, April 14-15, Heriot Watt Uni, Edinburgh
  2. 2. Structure • Introduction • Energy in Time Methodology – Model Development, Performance Analysis & Calibration; – Control optimisation • Case Study: Sanomatalo – Model development and calibration; – Sensitivity analysis; – Performance analysis (M&V); – Genetic optimisation; • Conclusions – Calibration process summary; – Conclusions & Future work.
  3. 3. INTRODUCTION Energy in Time Project Model Development & Calibration Control Optimisation
  4. 4. Company Background • Founded 1994 with HQ in Glasgow; • Offices worldwide; • Focused on delivering sustainable solutions from building to city-scale; • Main software: – IES-VE (Building simulation) – IES-ERGON (Building operations)
  5. 5. Energy in Time Overview Simulation based control for Energy Efficiency operation and maintenance • Real building data (BMS / Sensor) • Detailed building energy models • Predicted profiles (Occupancy / Weather) Calibration • Scripting API with Models / Profiles • Real-time prediction & control optimisation • Operational plan generation (OPG) Optimisation Programme: EeB.NMP.2013-4 Project reference number: 608981 Project acronym: Energy IN TIME Starting date: 1 October 2013 Duration: 48 Months
  6. 6. Model Calibration  Building energy models may be used in all phases of BLC from design to commissioning and operation. However, for operational use, there is a need to address any discrepancies between design performance and actual performance;  Building Model Calibration is the process of improving the accuracy of simulation models to reflect the as- built status and actual operating conditions;  Calibration performance assessed using standard statistical indices: 𝑀𝐵𝐸 % = 𝑖=1 𝑁 𝑝 𝑚 𝑖 − 𝑠𝑖 𝑖=1 𝑁 𝑝 𝑚𝑖 𝐶𝑉 𝑅𝑀𝑆𝐸 % = 𝑖=1 𝑁 𝑝 𝑚 𝑖 − 𝑠𝑖 2 𝑁𝑝 𝑚
  7. 7. Prediction / Optimisation  Prediction algorithms are required in order to determine future trends over short control time- frames based on historic data;  Control Scenarios: Prediction profiles, in conjunction with detailed calibrated simulation models are used to derive building performance predictions for a range of control scenarios;  Optimisation algorithms are used to determine the best course of action for a given set of objectives (e.g. Minimise cost / CO2) and constraints (ensure all zones within comfort threshold)
  8. 8. METHODOLOGY Overview Model Development & Calibration Control Optimisation
  9. 9. Methodology Overview • Three phases for project implementation: – Stage 1: Model Development and Calibration; – Stage 2: Model Re-calibration; – Stage 3: Control Optimisation. Static Model Parameters Model Profiles <FFP> Building Operational Data <SCAN> Base Model <VE> Sensitivity Analysis <PB+Python> Update Model Performance Criteria Met NO Calibrated Base Model <VE> YES Re-calibrated Operational Model Performance Criteria Met? YES Automatic re- calibration of Input Profile <Optimise> NO Model Variant 1 Model Variant 2 Model Variant 3 Scenario Modelling Optimal Control DSS Model Variant 2 • Three tiers for calibration / measurement:
  10. 10. Static Model Parameters Model Profiles <FFP> Building Operational Data <SCAN> Base Model <VE> Sensitivity Analysis <Python> Update Model Performance Criteria Met NO Calibrated Base Model <VE> YES Stage 1: Model Calibration: In this phase we develop a Base Model of our building or pilot area, using available historic performance data about the building (static parameters and operational profiles). Uncertainty- weighted sensitivity analysis is used to guide the model update process until performance criteria (risk/accuracy) are met. At this point, we have a Calibrated Base Model
  11. 11. Calibrated Base Model <VE> Re-calibrated Operational Model Performance Criteria Met? YES Automatic re- calibration of Input Profile <Optimise> NO Stage 2: Model Re-calibration: As the model will be used during building operation, it is necessary to regularly assess performance criteria and re-calibrate the model if performance drift occurs. In this phase, uncertain model profiles (e.g. occupancy, infiltration) will be adjusted automatically using an optimisation function. This is known as the Calibrated Operational Model and may be used to make reliable predictions for ongoing building operation and control.
  12. 12. Re-calibrated Operational Model Model Variant 1 Stage 3: Control Optimisation: In this phase, we introduce the concept of model variants, which represent significant changes to the calibrated base model (e.g. CV vs VAV). Each model variant may be run on the Apache cloud, under different scenarios (UGR). The results of these model scenarios will provide a control DSS for the building manager. Model Variant 2 Model Variant 3 Scenario Modelling Optimal Control DSS
  13. 13. CASE STUDY: SANOMATALO
  14. 14. Sanomatalo – Active Model
  15. 15. Sensitivity Analysis Normalised Sensitivity Index Parameter Total Energy [MWh] Total System Energy [MWh] Boilers Energy [MWh] Chillers Energy [MWh] Room Air [C] Overall AAHX_latent_effectivenss 0.002 0.002 0.001 0.003 0.000 0.001 AAHX_sensible_effectivenss 0.905 0.905 0.792 0.100 0.371 0.615 air_flow 1.000 1.000 0.060 0.116 0.079 0.451 conductivity_ceiling 0.055 0.055 0.076 0.085 0.080 0.070 cool_setpoint 0.000 0.000 0.000 0.000 0.000 0.000 equipment_gain 0.103 0.039 0.204 0.141 0.120 0.121 glazing_conductivity 0.057 0.057 0.151 0.047 0.117 0.086 glazing_transmittance 0.130 0.130 0.393 1.000 0.280 0.387 infiltration 0.315 0.315 0.893 0.230 0.461 0.443 lighting_gain 0.244 0.163 0.639 0.359 0.322 0.346 occupancy_gain 0.010 0.010 0.143 0.166 0.151 0.096 radiator_max_timestep 0.001 0.001 0.000 0.001 0.000 0.001 radiator_midband 0.568 0.568 0.720 0.317 1.000 0.635 radiator_panel_weight 0.003 0.003 0.000 0.003 0.000 0.002 radiator_radiant_fraction 0.008 0.008 0.010 0.011 0.003 0.008 radiator_water_capacity 0.003 0.003 0.001 0.005 0.000 0.003 steam_humidifier_humidity 0.003 0.003 0.000 0.008 0.000 0.003 supply_temp 0.587 0.587 1.000 0.315 0.515 0.601 Sensitivity analysis was carried with respect to parameter impact on five key model outputs: • Total energy [MWh] • Total System Energy [MWh] • Boilers Energy [MWh] • Chillers Energy [MWh] • Room Air Temperature [oC]
  16. 16. Measured and Simulated data were compared for the calibration period for the following output parameters:  Heating Coil Load (kW) - Hourly  Boiler Load (kW) – Hourly CVRMSE NMBE Sum of Diff ^2 2821.505 Sum of Diff 81.76972 No. Samples 409 n-p 408 Mean Observation 20.805 kW Mean Observation 20.805 kW CVRMSE 12.624 % NMBE 0.963 %  Mean Bias Error (MBE) (%) 𝑀𝐵𝐸 % = (𝑚𝑖 − 𝑠𝑖) 𝑁 𝑝 𝑖=1 (𝑚𝑖) 𝑁 𝑝 𝑖=1  Coefficient of Variation of Root Mean Square Error CV(RMSE) (%) 𝐶𝑉 𝑅𝑀𝑆𝐸 % = (𝑚𝑖 − 𝑠𝑖)2𝑁 𝑝 𝑖=1 𝑁𝑝 𝑚 Performance Analysis
  17. 17. Calibration – Manual Update Based on inputs from results visualisation and sensitivity and performance analysis, the model calibration focused on reviewing the following model parameters: • Electrical metering & weather data; • Occupancy profiles; • Adjacent conditions; • HVAC equipment.
  18. 18. Genetic Optimisation Genetic Optimisation is used to further refine the model by automatically modifying static input parameters and profiles. generation = 114 objectives variables NMBE CVRMSE supply_temp infiltration lighting_gain AAHX_sensible_effectivenssair_flow radiator_midband 0.00 20.55 1.49 0.89 1.10 45.71 0.92 22.90 0.00 20.53 1.45 0.77 0.92 43.98 0.94 22.81 0.00 20.48 1.46 0.88 0.72 45.69 0.92 22.88 0.01 20.46 1.47 0.91 0.84 45.57 0.90 22.88 0.02 20.45 1.48 0.78 1.01 44.52 0.96 22.82 0.02 20.45 1.48 0.78 0.65 44.52 0.96 22.82 0.04 20.45 1.50 0.77 1.31 43.94 0.94 22.82 0.04 20.45 1.50 0.77 0.95 43.94 0.94 22.82 0.04 20.45 1.50 0.77 0.95 43.94 0.94 22.82 0.04 20.45 1.50 0.77 1.27 43.94 0.94 22.82 0.05 20.42 1.49 0.87 0.51 45.83 0.94 22.88 0.05 20.37 1.47 0.89 0.51 45.71 0.92 22.88 0.09 20.36 1.45 0.85 0.51 45.59 0.94 22.88 0.09 20.36 1.45 0.85 0.58 45.59 0.94 22.88 0.18 20.36 1.46 0.86 0.51 45.66 0.94 22.88 0.19 20.34 1.45 0.91 0.51 45.59 0.92 22.88 0.30 20.32 1.47 0.90 0.51 45.66 0.94 22.88
  19. 19. Final Measurement & Verification TABLE 1: FINAL CALIBRATION PERFORMANCE METRICS - SANOMATALO Performance Criteria Mean Observation (kW) Weighting NMBE (%) CVRMSE (%) Heating Coil 20.56 0.62 0.96 12.62 Boiler 12.86 0.38 2.07 21.71 Overall 33.42 1.00 1.39 16.12
  20. 20. CONCLUSIONS Calibration process summary Conclusions Future Work
  21. 21. Calibration Process Summary Calibration Process employs a number of techniques to improve model calibration accuracy and efficiency:  Structured guidance for model development;  Standard procedures for performance assessment;  Real ‘free-form’ building profiles;  Sensitivity analysis;  Optimisation of static and dynamic building parameters;
  22. 22. Conclusions • There are many tools and methods available to aid model development and calibration – lack of clear guidance on calibration requirements and standards; • Hybrid method combines real building data with model physics to provide more accurate simulation with reduced time to implementation. When used appropriately, may offer an excellent alternative to full simulation models; • Statistical and graphical analysis provides a means of structuring model development, and assigning time and resources more effectively (e.g. Sensitivity, Uncertainty and Performance analysis); • Optimisation methods provide a robust means of refining parameter estimates. Need to be used with caution to avoid ‘tuning’ parameters incorrectly; • Access to a real building performance repository could help improve profile estimation and predictions;
  23. 23. Future Work • Complete testing of approach for four EU sites: – Test Site 1: Airport in Faro, Portugal – Test Site 2: Office and Test Labs in Bucharest, Romania – Test Site 3: Commercial and Office in Helsinki, Finland – Test Site 4: Hotel in Levi-Lapland, Finland • Integrate cloud simulation models with real building data streams for automated model performance analysis and re-calibration (where required); • Test and deploy operational plan generator (OPG) on pilot sites;
  24. 24. Thank you! Daniel Coakley BE PhD CEM MIEI MEI Research Fellow, Integrated Environmental Solutions Ltd. Adjunct Lecturer, National University of Ireland Galway Secretary, ASHRAE Ireland Email: daniel.coakley@iesve.com Web: www.iesve.com CIBSE Technical Symposium 2016, April 14-15, Heriot Watt Uni, Edinburgh

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