Electrical energy requirement for air conditioning and space cooling subsystems of Building
Management Systems (BMS) has increased at 4% of average steps per year since 2000, doubling
as the increment compared to water heating and lighting subsystems. Air conditioning and
space cooling subsystems account for approximately 20% of total BMS electricity consumption
or 10% of global electricity consumption according to the International Energy Agency (IEA).
As identified, within the BMS and air conditioning subsystem, chiller plants are the most
energy-intensive component that needs to be optimized to improve performance to reduce
energy costs with increasing cooling demand.
A chiller is the main component in an air conditioning system that changes the temperature
of a cooling liquid whose temperature is controlled by a refrigerant cycle and capable of
supplying a chilled liquid stream continuously while circulating it through the device.
There can be multiple chillers of different capacities and technologies within a chiller
plant with different overall system configurations.
Most modern commercial chiller plants consist of multiple chiller units with different
individual capacities in parallel configurations other than series and composite
configurations. When it comes to the operating strategy, the sequencing strategy is commonly
used to operate multiple chillers to supply chilled water for fulfilling daily cooling
demand. The sequencing strategy is based on conventional mechanical systems and equations
built on oversimplified assumptions which change the on/off condition of chiller units to
cover instantaneous cooling demand. The conventional sequencing strategy is sub-optimal,
more focused on supplying the instantaneous cooling demand with a minimum number of chiller
units in operation rather than operating it most efficiently.
Data-driven chiller plant optimization methods are giving promising results when it comes to
improved efficiency, energy saving and minimized machine ageing through a proper combination
of modern machine learning techniques and air conditioning domain expertise. Research-based
advanced chiller sequencing algorithm with real-time data acquisition and cooling load
prediction method shows the highest energy saving among other optimization methods.
As a technical overview of the above-mentioned method, the daily cooling demand forecast is
populated for the next 24 hours using a machine learning model based on historical data and
other environmental factors as input for optimal sequencing and various set point selection
algorithm, which is constructed as a graph optimization problem, generates optimal sequence
and set points under restrictions of minimum cooling fluctuations, uptime/downtimes and
other critical constraints. Also, this optimization method is based on dynamic data coming
from modern Internet of Things (IoT) systems or integrated sensor systems for real-time
The major benefit of applying data-driven optimization on a chiller plant is its capability
of performing well in dynamic environments and varying parameters which are extremely
difficult to model using a physical mechanical system since its dynamic behaviour.
Data-driven systems are highly capable of handling such scenarios and are compatible with
physical systems. These optimization methods are more focused on optimizing existing
resources rather than implementing another set of physical resources.
The investment in data-driven chiller plant optimization systems is much more cost-effective
compared to installing new chiller plants or spending a high amount on maintenance due to
avoidable machine ageing. As for the reference, research work and simulations done on the
above data-driven chiller optimization method proved that it can be outperformed the
conventional chiller sequencing method through entire cooling demand and cause 25% of energy
savings, depending on the efficiency of the conventional chiller sequencing method.
Xeptagon is currently undergoing an initial assessment of a chiller plant optimization
project for a large shopping mall complex in Hong Kong. The goal is to use IoT-coupled
data-driven optimization techniques to reduce energy costs.
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