1. Investigating the integration of dynamic thermal simulation with
building systems controls for energy efficiency/management
Anthony Maitos, Dr. Paul Strachan.
Department of Mechanical Engineering, University of Strathclyde in Glasgow
Introduction
Dynamic thermal simulation is currently well accepted as an enhanced design The initial phase of the study will involve setting up small scale experiments to
engineering tool. Similarly, building energy management systems (BEMS) are commonly link sensed inputs to, and controlled outputs from, simulation models. These initial
installed in buildings. This research topic focuses on integrating these, using real-time experiments will comprise ESP-r, a validated building simulation tool, LabVIEW as a
simulation to enhance building systems' controls, for example optimum start and night BEMS emulator, and an experimental representation of a building containing sensors
ventilation control. and actuators. If this is successful, future work will include developing control
The new feature of the research is to investigate the possibility of linking physically- algorithms and testing on real buildings.
Actuator
based simulation programs to operational controllers
Methodology - Implementation Sensor
Create building simulation model in ESP-r Precision integrated circuit thermal sensors are used
for temperature sensing, a 5V DC logic input optical
power relay and a lamp provide thermal output.
Provide model with actual building properties
user profiles, local climate, sensors/actuators,
A USB DAQ (12-bit) provides the wired
HVAC plant capacity.
IO, between the physical model and a PC
Setup building sensors and actuators. Link A LabVIEW routine was developed to allow
them via IEEE.802.x. or wire them to PC. communication between wired or ZigBee™
enabled sensors and a PC.
An in process database is used to facilitate A model was made in ESP-r, for which the
data exchange with the instrumentation control signal is obtained, and a physical
representation of the model has been created
Simulation Model
Building Model is calibrated to
compensate for the uncertainties in air Initial Results
exchanges, thermal bridges, ageing, etc.
Research on the subject has
begun only the past months,
Input control strategy and desired thus the results are yet
time for considered zones, to obtain e.g. minimum. The graph shows
Optimum start time for control strategy the optimum start time to
obtain the desired temperature
A control interface initializes data acquisition, of 26oC in the zone at 10.00
.
coordinates data exchange & initiates simulation am. Simulation run with a 10
minute timestep.
A future climatic file is
prepared for use in simulation
Current state of research and future work
Short term future work includes optimisation of the existing start/stop algorithm;
Yes
incorporation and testing of other control strategy algorithms (e.g. night ventilation). The
experimental phase (in the longer term) will begin with test cells and later an actual
Yes Control signal building. The research has three deliverables:
Simulation Loop
time much earlier
calculates control signal The first one is to investigate the possibility of linking physically-based simulation
than current time.
programs to operational controllers.
No The second is the identification of an appropriate machine learner algorithm to allow the
No
simulation model to self-calibrate.
Delay Control Signal.
Change parameters of The successful achievement of the previous deliverables will establish a third one: a
control strategy file framework to use ESP-r as a condition monitoring and a Fault Detection and Diagnosis
Control Signal is sent
predictive platform, That effectively means that ESP-r shall be utilised within a building
SCADA, proving the concept of adopting simulation as the “brains” of a BEMS, and using
it from the initial design stage, to plant calibration and throughout the building’s operational
lifespan.
References
1. J. A. Clarke, J. Cockroft, S. Conner, J. W. Hand, N. J. Kelly, R. Moore, T. O’Brien and P. Acknowledgements
Strachan, 2002. Simulation-assisted control in building energy management systems. I would like to thank Georgios Kokogiannakis, Dr. Jon Hand and Monica Lever for ESP-r
Energy and Buildings, Vol. 34, 933-940, . related advice, Linux tips and helpful discussions, as well as Konstantinos Kalovrektis for
2. J. A. Clarke, S. Conner, G. Fujii, V. Geros, G. Johannesson, C. M. Johnstone, S. Karatasou, his LabVIEW related assistance. Also to Pat McGinness and John Redgate for laboratory
J. Kim, M. Santamouris, P. A. Strachan, 2004. The role of simulation in support of assistance,. Funding for this project was provided by T.E.I. of Piraeus.
Internet-based energy services, Energy and Buildings, Vol.36, Issue 8, 837-846.
3. S. Conner, 2003. Distributed Dispatching for Embedded Generators, Ph.D. Thesis, ESRU,
University of Strathclyde, Glasgow.
Further Information
For further information, please contact antony.maitos@strath.ac.uk
4. P. N. Christias, A. Maitos, E. Vogklis, 2007. A complete software application providing
More information on this and related projects can be obtained at
ESRU
automated measurements storing, monitoring and feedback for dispersed environmental
http://www.esru.strath.ac.uk.
sensors. Proceedings of PCI2007, Vol . B_635-649, Patras, Greece. Poster is available at http://personal.strath.ac.uk/antony.maitos