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Calibrated operational models of existing buildings for real-time support and optimisation

By Edwina Cramp on Monday 23 May 2016

Daniel Coakley presented at the recent CIBSE Symposium on “Integration for whole life building performance.” His session looked at the “Development of calibrated operational models of existing buildings for real-time decision support and performance optimisation.” Building simulation tools are commonly used in design for performance appraisal and optimisation. However, numerous studies have found that actual building performance often deviates significantly from simulation predictions.

You can view the presentation here:

Paper
There is also an accompanying paper, which proposes a detailed framework to produce calibrated operational models, which can support operational decision-making, and real-time control optimisation.

The approach centres around a three-tier calibration process:
• Tier 1 focuses on Building level (Demand-side) variables (e.g. occupancy, equipment, infiltration).
• Tier 2 focuses on system-level (HVAC) model components (e.g. heating / cooling coil capacities). In this phase, we use detailed building data combined with genetic optimisation techniques to calibrate relevant input parameters. In the case where system performance modelling is not necessary, we use free-form profiles (i.e. measured building data) to supplement these model components. Once system-level noise has been eliminated.
• Tier 3 calibrates the remaining plant-level parameters (e.g. central plant, electricity consumption, etc.).

The approach is supported by two novel developments:
(1) Free-form profiles: These are actual historic trends from existing building controllers, which are used to supplement model components where appropriate;
(2) Genetic Optimisation algorithms are utilised to efficiently navigate the solution space to reduce discrepancies between the model and actual system performance. The proposed calibration approach builds upon prior research efforts to standardise the calibration process using evidence-based model development, combined with sensitivity and uncertainty analysis.

Click here to read the full paper.


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