Trial results from a model predictive control and optimisation system for commercial building HVAC
Introduction
This paper presents a supervisory control and optimisation system for commercial HVAC systems, aimed at minimising energy consumption and occupant thermal discomfort, and an accompanying online occupant comfort feedback tool which can be installed on occupant computers.
Occupant feedback from the comfort feedback tool is used to assess the impact of the optimised HVAC control strategy, to fine-tune the standard comfort model used in the optimisation process and to inform users of the state of the HVAC system, such as when natural ventilation or economy modes are active.
The supervisory HVAC control and optimisation system (referred to hereafter by the authors’ internal designation ‘OptiCOOL’ interfaces with a commercial building management & control system (BMCS) to read required data from HVAC zones and air handling units (AHUs) and to perform required control actions. It learns a model that can be used to predict zone conditions and comfort levels using information about HVAC power consumption and weather, which is used to discover the optimal power consumption schedule throughout the day-balancing cost, energy and emissions while maintaining comfort. The control module then overrides zone or supply air set-points to balance the heating/cooling power supplied to each zone and to track the best power profile from the optimisation.
Both systems were installed in two office buildings on the east coast of Australia and trialled over two months during winter to evaluate their energy savings potential. Thermal comfort levels were monitored throughout the trials to ensure consistency with pre-trial levels.
Section snippets
Background
Model predictive control (MPC) has been widely used for some form of building HVAC optimisation. The majority of work to date concentrates on examining control strategies for specific subs-systems [1], applying offline optimisation of particular buildings or building configurations using simulation models [2], or considers only specific approaches such as set-point resetting, peak demand limiting or load shifting [3], [4], [5], or examines techniques using small-scale test beds [6], [7]. Whole
Thermal comfort
Human perception of thermal comfort is highly subjective, and modelling it accurately for the purposes of predicting occupant comfort from measured environmental conditions is an active area of research, with de facto standard models having been shown to bias predictions such that excess cooling energy is often consumed unnecessarily [22]. Given that the primary aim of building HVAC systems is to maintain a level of occupant thermal comfort, it is critical to have an accurate comfort metric by
Trial details
The system was deployed and experiments run at two sites during winter 2011 (July–August) in two buildings on the east coast Australia.
The first building was a 3-floor 3322 m2 office building in Newcastle, New South Wales (NSW) with an under-floor air distribution system driven by 15 AHUs. The water loops to each AHU were supplied by two centralised chillers and a Combined Heat and Power (CHP) system with two gas micro-turbines and two gas boilers. The second building was a 3-floor 1808 m2 office
Conclusions
We have presented a novel method of optimising the operation of commercial HVAC systems using model predictive control, to minimise a weighted combination of operating cost, greenhouse gas emissions and occupant thermal comfort. The system was trialled in two buildings in Newcastle, and Melbourne Australia during winter 2011 to compare performance to standard building management and control systems.
Performance results obtained from the Newcastle Office Wing over winter 2011 show an average
Acknowledgement
This research was funded in full by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Energy Transformed Flagship, Australia.
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