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Artificial Intelligence Approach to Energy Management and Control in the HVAC Process An Evaluation Development and Discussion.

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Artificial Intelligence Approach to
Energy Management and Control in
the HVAC Process: An Evaluation,
Development and Discussion
Hong Zhou, Ming Rao* and Karl T. Chuang
Department of Chemical Engineering, University of Alberta,
Edmonton, Alberta, Canada, T6G 2G6
Computer control technology in heating, ventilating and air conditioning (HVAC)
systems has evolved in two technical phases: central control and direct digital control
(DDC) with central monitoring function. Many control strategies have been
developedfor energy conservation in HVAC energy management and control systems
(EMCS) and these are reported in the literature. An artificial intelligence approach
to the EMCS in HVAC process is proposed in this paper, Two real time expert systems
for energy-saving operation and indoor setting are developed and integrated with an
adaptive control strategy. Further, a configuration of an integrated intelligent system
for intelligent building management is proposed, in which building management
functions are coordinated by a meta-system. The applications of artificial intelligence
(AI) techniques in a HVAC process give a new avenue for the development of EMCS
in the HVAC industry.
Introduction
Computer control technology has been applied to heating, ventilating, and air
conditioning (HVAC) for more than twenty years. During this period, the
technology has evolved in two phases: central control and distributed direct digital
control (DDC) with central monitoring. In the 1970s, centralised control systems
using minicomputers were implemented in the HVAC process. This brought great
control flexibility and energy saving opportunities to the HVAC industry, but
suffered from poor reliability and the high cost of the computer system. In the
early 1980s, a growth in computer applications took place in all HVAC areas
because of the introduction of the microcomputer. Direct digital controllers were
then distributed throughout the buildings. The functions included at this level
could be either occupant comfort control or remote unit control and monitoring.
Energy management and control (EMC) are performed by a central
microcomputer. With a distributed controller, system reliability was improved and
* To whom correspondences should be addressed.
Developments in Chemical Engineering, Vol. 1, No. 1, Page 42
Artificial Intelligence Approach
43
computer-based energy management and control systems (EMCS) have recently
been accepted as standard control features in large commercial buildings.
What advantages could DDC with a central monitoring system achieve by
using advanced computer technology?* What is the third technical phase for
HVAC control systems? In this paper, we will first briefly review the current
energy conservation control strategies, and address the problems encountered
within the current EMCS of a HVAC system. Second, an artificial intelligence
method for energy management and conservation control is presented, and its
applications are outlined. Furthermore, an integrated intelligent system is
introduced into intelligent building technology. Finally, the third technical phase
in HVAC control systems is discussed.
Review of HVAC Energy Conservation Control Strategies
The EMCS technology has been utilised in the HVAC industry for more than ten
years. Approximately 70% of all energy management systems are used only for
the single function of HVAC energy management control. The HVAC energy
control strategies may be classified as follows:
Time scheduling
This is the most effective energy-saving control strategy in the early use of EMCS
in HVAC. It turns equipment ON or OFF at preset times. Normally, this strategy
turns equipment off when it is not needed or it gives a better match of the loads.
For example, scheduling HVAC equipment to run during occupied hours;
'dayhight setback' which sets low temperature when heating is used to maintain
temperature, and sets high temperature when cooling is used to maintain
temperature at night.2 Seven different equations are compared by Seem el al? to
determine the correct time to return from night or weekend setback. It was
observed that the return time was linearly related to the outdoor temperature when
the initial room temperature was at the setpoint during night or weekend setback.
Linear or quadratic equations with a weighting function provided the best forecast
of return time. Time scheduling also allows HVAC equipment to be off-duty to
avoid electrical peak time.
Advanced HVAC process control
Optimisation, adaptive control and predictive control have been applied to HVAC
p r o c e ~ s e s . ~Since
- ~ chiller plants consume a very large amount of energy in
HVAC systems, chiller plant optimisation has been the focus of many research
studies. Some important optimal control strategies have been proposed for the
chiller.g-" B r a ~ n ' ~ investigated
''~
the methodologies for optimal control of
chilled water systems without storage, he proposed a component-based nonlinear
optimisation algorithm and simpler system-based near-optimal control. Adaptive
control techniques are attractive i n HVAC applications because process
characteristics change widely due to variations i n weather and building
occupancy.
It has been demonstrated by Dexter and HavesI6 that adaptive control can cope
with many problems encountered in HVAC applications, and requires far less
commissioning efforts than conventional controllers. The control performance is
superior to that of a manually tuned PID controller. The long term behaviour and
energy savings achieved by using adaptive control in HVAC processes are still
I49l5
44
Hong Zhou, Ming Rao and Karl T. Chuang
under investigation.16 By predicting outdoor and indoor temperatures, as well as
building thermal resistance and capacity, we can prevent the loss of energy in
preheat or precool cycles, and determine the best recovery time from night
~ e t b a c k ,as~ well as the optimal start/stop sequence. In addition, if an indoor
temperature swin is allowed, a large saving can be achieved by the predictive
control strategy. 1.B
Operations planning
There are several alternative air handling processes which achieve an indoor
setpoint in a HVAC system. The option of energy conservation operations is based
on the following methods:18
(a) Economy-cycle outside-air control: This strategy is to use outside air for
cooling whenever possible, thus minimising energy use in the refrigeration
cycle.
(b) Enthalpy control: This strategy is achieved by calculating the enthalpy of air
being processed and choosing the minimum enthalpy cost for air handling
operation. The calculation of the enthalpy is not an easy task. Alternatively,
we can compare the enthalpy cost of air processing operations on the
psychrometric chart in order to select the minimum-enthalpy cost operation.
(c) Conversion of dual-duct to variable-air-volume (VAV): Replacing a dual-duct
system by a VAV system is a very effective energy conservation technique.
Mixing loss and fan horse power savings can be made by adding fan volume
control.19.20
(d) Energy-source shutdown: Depending on the season, shutting down heating or
cooling sources also provides energy conservation. However, the shutdown
should not cause the loss of environmental control.
Energy conservation operation planning of the HVAC process mainly relies on
the weather, room set, HVAC system structure, and so on. It often confuses
operators. Since there are so many alternative HVAC constructions, a global
solution may be very difficult to obtain.
Comfort technology
Comfort research has provided a number of alternatives for energy
We believe that 'comfort' and 'energy' could be simultaneously
optimised through the use of operation strategies which consider the dynamics of
comfort and control system. "Dynamic control" is a recently developed energy
conservation control strategy which utilises comfort technology, having been
original1 conceived back in the mid-1970s. Hartman23 and Colburn and
Harman2' investigated this advanced control strategy and pointed out that seven
key variables had a significant effect on human comfort. These were dry bulb
temperature, relative humidity, mean radiant temperature, air velocity, clothing,
activity (metabolic rate) and exposure time. Compared to other control strategies,
dynamic control allows user interaction and uses all seven comfort variables. It
combines three concepts: system-free cooling, interior temperature drifting and
thermal flywheeling.
This dynamic-control strategy introduced two new control concepts into
HVAC control. First, it integrates all controlled HVAC components into a
coordinated, but continually changing control strategy. Second, it continuously
anticipates upcoming weather and occu ancy conditions in order to develop the
control law. A case study by Spratt et al.% illustrated that dynamic control offered
less space-temperature control than normal control, and the comfort conditions
Artificial Intelligence Approach
45
were improved. Thermal comfort dynamic control methodology has been
implemented into a commercial product called "Touchstat". The test results
conducted on two residences showed a 55% electricity saving, equivalent to
$2.58 m-2 ~ r - ' . ~ ~
Comfort technology has not yet been fully utilised for energy conservation.
Many users and designers are still unaware of the opportunities available for
energy conservation. Comfort technolo!;
education has been suggested for
transferring this knowledge to the public.
New energy eJjcient equipment
Heat-pump air conditioners, solar energy heaters, and inverter control systems are
the more recently developed energy-efficient HVAC eq~iprnent.~'Most of these
items use microprocessor-based control and significantly reduce the energy
consumption.28 They have often been used in practical applications. Heat pumps
are used in residential air-conditioning systems with advanced control, i.e.
inverter control system, defrost control, setback thermostats, variable-speed fan
control, etc., and result in a 20-40% energy ~ a v i n g . ~ ~ - ~ '
From the system science viewpoint, the current energy management and
control strategies used in HVAC processes are a collection of remedies or recipes,
containing neither coordination nor integration. The reduction i n energy
consumption was easy to achieve in the early stage with only a small capital
investment. But these simple remedies have now been exhausted. It is very
difficult to model the HVAC process. Most problems i n the EMCS are
ill-structured, and therefore difficult to handle by a mathematical approach.32733
Artificial Intelligence Approach to the EMCS
Recently, attention has been focused on A1 applications in HVAC processes.3341
Many ill-structured engineering problems that deal with non-numeric information
and non-algorithmic procedures are suitable for applications of artificial
intelligence (AI) technique^.^^ The A1 approach provides a programming
methodology for solving ill-structured problems and allows use of heuristics.
Expert systems is the main field of artificial intelligence research. An expert
system is a specific computer program that acquires and codes the knowledge
from the human experts in order to solve ill-structured problems. An expert
system usually consists of three components: a data-base to hold the data for the
system; a knowledge-base to contain the knowledge about the area of the problem;
and an inference-engine to determine which rules are relevant to given data. The
structure of an expert system is shown in Figure I . Knowledge is mainly collected
from the available literature and is confirmed by human experts. A typical rule is
presented as: "IF (condition), THEN (action)."
As most of the EMCS problems in an HVAC process are ill-structured, an
expert system approach provides a suitable methodology.
An expert system approach to the EMCS of a HVAC process is proposed here,
it investigates real-time expert systems for energy conservation control in the
HVAC process. It aims at improving energy efficiency in new or existing
residential houses and commercial buildings. Specifically, it is required to apply
A1 techniques to the EMCS for the economic operation of HVAC. The primary
objectives of the proposed approach are summarised as follows:
46
Hong Zhou, Ming Rao and Karl i? Chuang
--r'
KNOWLEDGE
Figure 1 Structure of an expert system.
(1) to develop a real-time decision making system for comfortable and energy
efficient settings of temperature and humidity in a HVAC process;
(2) to develop a real-time expert system for energy-saving operation planning in
various HVAC systems;
(3) to design and implement the real-time energy conservation expert control
systems for the HVAC process in residential houses and commercial
buildings;
(4) to integrate the above systems in an intelligent environment;
( 5 ) to investigate new energy management and control strategies for the HVAC
process.
A basic framework for the intelligent control system is hierarchically
constructed to provide indoor settings, air-processing operation planning, and
adaptive control.
(1) Development of a real-time decision-making system for indoor temperature
and humidity settings.
The indoor settings play an important role in both thermal comfort and energy
consumption, however, they have not been fully utilised to reduce energy
consumption and improve comfort. It is recognised that a range of temperature
settings exist for which building occupants feel comfortable. We are currently
developing an expert system to provide real-time dccision-making for temperature
and humidity setting. The knowledge includes thermal comfort technology and
energy management, as well as "dynamic control" strategies. This expert system
can also provide off-line decision support to the operators.
(2) Development of a real-time expert system for energy-saving operation
planning in various HVAC systems.
A year-round HVAC system is usually made up of five or six items of control
equipment, and up to six air-handling operations: mixing, heating, cooling,
humidifying, dehumidifying, and air-washing. In addition, various HVAC systems
such as multi-zone, single zone, variable air volume, constant air volume, and
different combinations of the above systems are being used. The identification of
an optimal energy-saving operation is very complicated. An expert system for
Artificial Intelligence Approach
47
energy-saving operation planning in various HVAC systems has been developed
in an expert system developing shell, namely PC-plus. The expert system consists
of two levels of frames and external numerical computation. It can be used in
either real-time decision making or for off-line operation assistance.
(3) Implementation of an expert system for energy conservation control in a
HVAC process.
The decision-making system for indoor setting and the expert system for
energy-saving operation planning, as well as adaptive control technique, should
be integrated to provide this advanced control technology to the HVAC industry.
For a particular HVAC system, the energy-saving operation scheduling should be
provided on-line by an expert system based on the weather conditions, the indoor
setting, and the supplied air. An adaptive control technique will be used to set
suitable requirements for supplied air, and to control air handling equipment.
Because of the large differences between commercial buildings and residential
houses, the implementation will be carried out separately.
This new systematic approach provides an opportunity for functional
integration and coordination with the other systems in intelligent building. It will
also provide innovative HVAC process control incorporating energy efficient
operation and better comfort in buildings.
Integrated Intelligent System for Building Management
Modern building technology is moving towards "Intelligent Building".4349 An
"Intelligent Building" is one that provides a productive and cost-effective
environment through optimisation of its four basic elements: structure, s stem,
services, and management, as well as the interrelationships among them." The
building control system is the brain of intelligent building. Essentially, integration
is the key to intelligent building technology. The hierarchical structure for the
integration of a building management system is shown in Figure 2. This plan
divides system functions into two categories, as either 'integrated automation' or
'integrated communication'. The former contains functions such as fire detection,
security, lighting, HVAC, and environment control; while the latter holds the
foreground office functions of telephone, telex, electronic mail and data
communication. To accomplish such a complicated integration, it is necessary to
apply distributed intelligence techniques - an important field among artificial
intelligence techniques.
To facilitate integration for distributed intelligence systems, a "meta-system"
concept was suggested by Rao et al. .42,5' A meta-system performs the knowledge
integration and management, and provides the following integration functions:
(1) integration of knowledge from different disciplinary domains;
(2) integration of empirical expertise and analytical knowledge;
(3) integration of various objectives, such as research and development,
engineering design and implementation, process operation and control;
(4) integration of different symbolic processing systems (expert systems);
( 5 ) integration of the symbolic processing system and numerical computation
systems;
(6) integration of different information, such as symbolic, numerical and graphic
information.
The meta-system has six important functions in the integrated intelligent
system:
Hong Zhou, Ming Rao and Karl T. Chuang
48
Integration of building management function
Metesystem
I Integrated automation 1
ated communication
I
I
I
I subsystems I
I
CCTV
Figure 2 Hierarchical structure of the integration of building management.
(1) It is the coordinator used to manage all symbolic reasoning systems and
numerical computation routines in an integrated intelligent system.
(2) It distributes knowledge into separate expert systems and numerical routines so
that the integrated intelligent system can be managed effectively.
(3) It is the integrator which can easily acquire new knowledge.
(4) It can provide a near optimal solution for conflicting solutions and facts among
different expert systems.
( 5 ) It provides the possibility of parallel processing in the integrated intelligent
system.
(6) It can communicate with the measuring devices and the final control elements
in the control systems, and transform various non-standard inputloutput
signals into the standard communication signals.
The meta-system has been implemented in the C environment, namely Meta-C.
In Meta-C, the symbolic process follows the progress of the numerical process by
receiving posted information from the numerical procedure at several steps during
calculation. The symbolic process then has the option of allowing the numerical
procedure to continue, or changing some parameters, or aborting the procedure
entirely. This process competes favourably with a shallow-coupled process where
the heuristic process invokes a numerical routine via a procedure call, supplies the
necessary input information, and passively waits for the numerical process to
terminate, and then provides the required output.
The configuration of the integrated intelligent system has attracted significant
attention from both industry and academia, and is expected to lead to a new era
in the application of A1 techniques to engineering problems.42 Integration is a key
concept in intelligent building technology. The integration of building
management functions gives a new avenue for HVAC control systems. Many
HVAC professionals throughout the
are enthusiastically adopting the
concept of intelligent buildings, rather than deprecating it. The application of A1
techniques in HVAC energy management and control systems may give rise to a
third technical phase in the HVAC industry.
Artificial Intelligence Approach
49
Conclusion
C u r r e n t energy conservation control strategies are reviewed in this paper,
illustrating a lack of systematic organisation. An artificial intelligence approach is
proposed for energy conservation control in HVAC processes. W o real-time
expert systems are being developed; one chooses energy saving operations, and
the other sets indoor parameters by using comfort technology. Furthermore, an
integrated intelligent system is introduced into intelligent building, in which
b u i l d i n g m a n a g e m e n t f u n c t i o n s are c o o r d i n a t e d by t h e meta-system. The
applications of artificial intelligence techniques provide the new avenue for
EMCS development in the HVAC industry.
Acknowledgement
This project is financially supported by the Natural Sciences and Engineering
Research Council of Canada and Hardy Fund.
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Received: 24 May 1991; Accepted: 19 September 1991.
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