NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Calibration of a Building Energy Model to Energy Monitoring System Data
Using an Analytical Optimisation Approach
Daniel Coakley 1 , Dr. Padraig Molloy 1 , Dr. Paul Raftery 2
1 Dept. of Mechanical & Biomedical Engineering, NUI, Galway
2 Dept. of Civil & Environmental Engineering, NUI, Galway
d.coakley1@nuigalway.ie, padraig.molloy@nuigalway.ie, research@paulraftery.com
Abstract
The built environment accounts for approximately 40%
of global energy consumption and is responsible for 30-
40% of greenhouse gas (GHG) emissions. Energy
modelling tools such as EnergyPlus provide a means of
understanding and optimising energy performance in
buildings. This study focuses on the ‘calibration’ of
such models to actual measured data using an
analytical optimisation approach. Numerical multivariable
optimisation techniques will then be used to
analyse the calibrated model and optimise building
control strategies for enhanced energy efficiency and
occupant comfort.
1. Introduction
Whole building energy models provide a means of
understanding building operation as well as optimising
performance. Simulation tools, such as EnergyPlus,
represent continuous, stochastic processes in buildings
by discrete time-step, deterministic model estimations.
Due to the complexity of the built environment and
prevalence of large numbers of independent interacting
variables, it is difficult to achieve an accurate
representation of real-world building operation. By
‘calibrating’ the model to measured data, we can
achieve more accurate and reliable results. A review of
current literature on this topic has revealed that there is
no generally accepted method by which building energy
models should be calibrated.
2. Literature Review
Since the calibration problem is itself overparameterised
and under-determined, it is impossible to
find an exact, unique solution. A mathematical
formulation process has been suggested to find a
solution whereby a value and weighting is assigned to
certain known or measurable parameters. [1] By using
an objective function approach, the aim is to find a
solution which minimises mean square errors between
measured and simulated energy use data while
conforming to these weighted values. More recently a
methodology has been developed whereby best-guess
estimates are assigned to a heuristically defined set of
influential parameters. [2] These are then subject to a
Monte-Carlo (MC) simulation involving thousands of
simulation trials to find a set of promising vector
solutions. Simulations are carried out using the
template-based DOE-2 software and calibrated to data
attained from building audits as well as monthly utility
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bill information. This study provides an excellent basis
for further work on analytical optimisation of the
building simulation calibration process. However, this
approach has so far been limited to basic templatebased
simulation tools and only focuses on buildings
where limited design and energy-use data is available.
3. Proposed Methodology
This project will attempt to validate a similar analytical
optimisation approach to calibrate a more detailed
EnergyPlus model of a naturally ventilated building. A
thorough literature review has not found any previous
calibration studies for naturally ventilated buildings.
Thus, this will serve as a basis for future studies as well
as highlighting potential problems related to this type of
building. Long-term monitored data from the Building
Management System, measured data from an on-site
weather station, and numerous site surveys during the
calibration period will be incorporated into the
calibration methodology. A Building Energy
Simulation (BES) model of an existing 700m 2 library
will be developed. Data pertaining to the building
construction, systems and operating schedules will be
acquired. The model will be developed based on this
evidence and will be tracked using version control
software. Subsequently this BES model will be
calibrated using the proposed analytical methodology.
This will involve reducing the dimensionality of the
parameter space by performing a sensitivity analysis to
determine influential parameters and reasonable
parameter values. A two-stage MC simulation process
will then be used to find a realistic set of solutions that
satisfy the objective function. Finally, by isolating
controllable parameters identified in the initial
optimisation approach, it is proposed that the calibrated
model will be used to assist in the identification of
optimal operational and control strategies.
4. Acknowledgments
Research funded by NUIG College Fellowship
5. References
[1] Carroll, W.L., and R.J. Hitchcock. Tuning simulated
building descriptions to match actual utility data: Methods
and implementation. ASHRAE Transactions 99(2):928-34,
1993.
[2] Sun, J., and T.A. Reddy. Calibration of building energy
simulation programs using the analytic optimization
approach. HVAC & R Research 12(1):177-96., 2006.