ASHRAE IJHVAC 14-1：HVAC&R Research期刊文章

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"ASHRAE IJHVAC 14-1.pdf" 是一份由ASHRAE（美国采暖、制冷与空调工程师学会）发布的行业期刊或研究报告。这份文档可能包含了关于暖通空调（HVAC）与制冷领域的最新研究、技术发展、工程实践以及行业标准。 ASHRAE是全球领先的建筑环境科学和技术组织，致力于推动可持续的建筑环境实践。其成员包括工程师、建筑师、科学家、研究人员以及行业专业人士，他们共同致力于改进建筑物的能源效率、室内环境质量和可持续性。该文档的编辑团队由知名学者组成，其中包括Reinhard Radermacher博士，他是美国马里兰大学环境能源工程中心的教授和主任；James E. Braun博士，Purdue大学机械工程学院的教授；Alberta Cavallini博士，意大利帕多瓦大学技术物理系教授；Srinivas Garimella博士，乔治亚理工学院机械工程学院教授及可持续热系统实验室主任；Leon R. Glicksman博士，麻省理工学院建筑与机械工程系教授；以及Dongsoo Jung博士，韩国某大学机械工程系教授。这些专家的参与确保了文档内容的专业性和权威性。虽然具体内容没有给出，但可以推测文档可能涵盖以下几个方面的内容： 1. **能源效率**：讨论最新的HVAC系统设计，以提高能源效率，降低能耗，符合ASHRAE的能效标准。 2. **制冷技术**：介绍新的制冷剂、冷却系统和热管理技术，关注环境影响和能效。 3. **室内环境质量**：研究如何改善室内空气质量、温度控制和噪音管理，以创造更健康、舒适的居住和工作环境。 4. **绿色建筑**：探讨绿色建筑的设计策略，包括可再生能源利用、建筑材料的选择和建筑生命周期评估。 5. **研究进展**：发布最新的研究成果，如新材料、新方法在HVAC&R领域的应用。 6. **行业标准与指南**：更新和解读ASHRAE制定的相关标准和最佳实践指南，指导行业的合规操作。 "ASHRAE IJHVAC 14-1.pdf"对于暖通空调和制冷行业的从业者、研究人员以及关心建筑环境性能的人来说，是一份极具价值的信息来源，它提供了行业发展的最新动态和专业知识。通过深入阅读，读者可以了解到当前领域的热点问题和未来趋势，从而提升专业技能，推动技术创新。

Table

Summary of Main Optimization Techniques Used in Building

HVAC

Field

Techniques Strength Weakness Application Examples

Simple and easy to be

Often fails to obtain an optimal solution.

Wright and Hanby 1987; Sreedharan and

Direct search understood and implemented. Haves 2001; Braun and

Chatuwedi

No derivatives are required.

It is less computationally efficient.

2002; etc.

Sequential quadratic

Can efficiently handle a large number

Has to start from initial guesses and its Olson and Liebman 1990; House and

programming of inequality constraints.

convergence speed is affected by

Smith 1995; Kota et al. 1996; Sun and

its initial guesses.

Reddy 2005; etc.

Easy to be implemented since

Nonlinear

Lagrange

Local

Lagrange formula does not depend on

The convergence is

Hach and Katoh 2003;

Techniques

method the order in which the nodes are not always guaranteed. Chang 2004; etc.

arranged.

Conjugate gradient

Overall computational cost is small for

Less efficient and robust

method

compared to other technique,

large number of decision variables.

Nizet et al. 1984; etc.

i.e., quasi-Newton method.

The convergence speed is quite slow and

Univariate search

Simple and easy to be implemented.

it can not find the optimum values at

Hanby and Angelov 2000;

some cases.

Bassily and Colver 2005; etc.

Can provide a good

andlor a subgood

High computational cost is always

Branch and bound

solution. It is easy to incorporate any

required and it is possible to miss the

Sousa et al. 1997;

(B&B)

constraint into this method.

Chang et

al.

2005; etc.

globally optimum solution.

Nonlinear

Relatively easy to be implemented and

Global

Simulated annealing has strong ability to provide reasonably

High computational cost and memory

Koeppel et al. 1995;

Techniques

demand are always required.

good solutions.

Flake 1998; Chang et al. 2006; etc.

Evolutionary With high generalities and flexibili- Extensive computational cost and Huang and Lam 1997; Wang and Jin

algorithms and

ties, and there are also robust to find

memory demand are always required due 2000; Nassif et al. 2005; Lu et al. 2004,

genetic algorithm the global minimum. to high number of fitness evaluations. 2005b; Fong et al. 2006; Gc.

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Univariate search:

It is primarily developed for solving unconstrained nonlinear optimization

problems. A single variable is changed at a time to obtain its optimal value with respect to the

current values of all other variables of the optimization problem (Rao 1984).

Nonlinear Global Optimization Techniques

Branch and bound (23&B):

This is a tree-based search technique that is very popular for the solu-

tion of combinatorial optimization problems. The basic idea of this method is to build a tree that

contains all possible parameter combinations, and to search only the necessary part of this tree.

This method employs tests at each node of the tree, which allows one to cut parts of the tree, and

thus saves computational cost as compared with an exhaustive search (Nelles 2001).

Simulated annealing:

This is a stochastic method, and the basic principle of this method is

presented as follows: A

warm particle is simulated in a potential field. Generally, the particle

moves down toward lower potential energy, but since it has a non-zero temperature,

i.e.,

kinetic energy, it moves around with some randomness and therefore occasionally jumps to

higher potential energy. Thus, the particle is capable of escaping local minimum and possibly

finding a

globaI one. The particle is annealed in this process, and its temperature decreases

gradually, so the probability of moving uphill decreases with time. It is well known that the

temperature must decrease slowly to end up at the global minimum energy

(Nelles 2001).

Evolutionary algorithms and genetic algorithm:

Evolutionary algorithms take their inspira-

tion from natural selection and survival of the fittest in the biological world. They include a

search from a "population" of solutions. Each iteration process consists of a competitive

selection that discards poor solutions. The solutions with high "fitness" are "recombined"

with other solutions by swapping parts of

solution with another. Solutions are also

"mutated" by making a small change to a single element of the solution. Recombination and

mutation are used to generate new solutions that are biased toward regions of the space for

which good solutions have already been seen (Gray et al. 1997). The existing approaches to

evolutionary algorithms include evolution strategy (ES), evolutionary programming

(EP),

genetic algorithm (GA), and genetic programming (GP). They all share the same basic model,

but are considerably different from the ways of their representation (binary or real-valued),

the means of their selection (stochastic or deterministic), and the essentials of crossover and

mutation. Genetic algorithm is most popularly used among these algorithms.

Other Optimization Techniques

Other optimization techniques mean that some optimization techniques have been used in the

optimization problems in building HVAC systems. However, they are used for solving the

individual

andlor particular rather than typical optimization problems in HVAC systems. For

instance, a recursive numerical algorithm was used by Liu and He (1994) to optimize the thermal

comfort level in

air-conditioned room. The Newton-Raphson solution method was used by

Mullen et al. (1998) to solve the equations in an optimization problem in a room air-conditioning

simulation model. Most of these conventional techniques might be effective and

successfbl for a

particular and simple optimization problem, but may not be efficient and reliable for high

nonlinear and complicated typical optimization problems in building HVAC systems.

Most of these optimization techniques demonstrated their excellent performance for particu-

lar applications. The major difference among these techniques is that different optimization

techniques possess different computation efficiencies, while some techniques may be divergent

in some cases. Some optimization techniques may also result in a local optimal solution, and a

globally optimal solution is not always guaranteed. Among all of these techniques, genetic algo-

rithm (GA) is attracting growing attention of building professionals and has been widely used in

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academic research for global optimization (Huang and Lam 1997; Wang and Jin 2000; Chow et

al. 2002; Nassif et al. 2005; Lu et al.

2005b; etc.). GA is a result-based method, and no deriva-

tives are required during the calculation. This feature makes it possible to solve the complicated

and global optimization problems. However, the extensive computational cost and memory

demand may be an obstacle for online application of this technique. Further research of the

robustness and feasibility of this technique for practical application is essentially required.

In practice, optimization techniques should be selected based on the combination of the com-

plicity and characteristics of the system of concern, as well as the number of optimization vari-

ables involved for

particular optimization problem. The selected optimization technique

should have less computational cost and memory demand to meet the requirements of practical

application. The convergence should be always guaranteed.

should also have a simplified

structure and should be easy to be understood by the practicing engineers.

RESEARCH

AND

APPLICATION OF

OPTIMAL CONTROL STRATEGIES FOR HVAC SYSTEMS

Since control function is one of the major hctions of a BAS, the building society and profes-

sionals have made serious efforts toward the development and application of various control strat-

egies for HVAC systems. Figure

is a schematic of the scattered areas of the research and

development of the control studies in HVAC systems. It can be found that most research related to

HVAC system control focused on the local level control (area

l),

while relatively few studies

focused on the supervisory control (area 2). The reason is probably due to the easier implementa-

tion of local controllers in practice. Since sensor faults

and/or component degradations in HVAC

systems may cause significant energy consumption or increase overall system operating cost, the

research on fault detection and diagnosis (area

in HVAC systems has been becoming a large

research area and a hot research topic since the last decade. For a robust control strategy, it should

have the ability of fault-tolerant control, in which the system can be controlled properly even if

some faulty measurements

and/or system component degradations exist. There are numerous stud-

ies that specifically pertain to fault-tolerant control in control engineering. However, it is still in its

infancy in building HVAC field (area

4),

and the practical applications are rare.

Targeted at providing the satisfied indoor thermal comfort and healthy environment with the

least energy input or operating cost under dynamic indoor and outdoor conditions, many super-

visory and optimal control studies related to HVAC systems have been reported (Huang and

Lam 1997; Wang and Jin 2000; Wang and

Bumett 2001; Chow et al. 2002; Nassif et al. 2005;

Lu et al.

2005b; Sun and Reddy 2005; etc.). Chapter

of the 2003

ASHRAE Handbook-HVAC

Applications

(ASHRAE 2003) has provided a number of near-optimal strategies for cooling

tower fan control, chilled-water reset with fixed and variable-speed pumps, sequencing and

loading of multiple chillers, strategies for air-handling units, strategies for building zone temper-

ature setpoints, cooling thermal storage control, etc. The implementation issues of most of these

strategies are also presented. The readers are advised to go through this chapter for details, while

a number of strategies will also be addressed in this paper.

In this section, a comprehensive review of the research and development as well as applica-

tion of supervisory

and

optimal control in building

HVAC

field in last two decades is presented,

which intends to summarize most of such studies completed up to date. All studies were

reviewed according to the classification schematic of supervisory control methods presented in

the "Supervisory Control Methods" section.

Physical Model-Based Supervisory Control Strategies

few studies related to the supervisory control in building HVAC systems use dynamic

and/or static governing equations and detailed andlor simplified physical models to construct the

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Research

Control Strategies

Research

FDD

Strategies

1--Local control 2--Supervisory control

3--FDD (Fault Detection

and

Diagnosis)

--Fault-tolerant control

.....---...-.-...*----*-----*-----.---------------------------------------------------*-----------------------*------

)

Figure

Schematic of scattered areas of the research and development on control

strategies for building

HVAC

systems.

supervisory control methods. All these governing equations and physical models are derived

based on fundamental laws of energy, mass, heat transfer, momentum, flow balance, etc.

Kaya et al. (1982) introduced a thermal model based on the governing equations for the space

along with an index of energy use to develop the optimal control method for an HVAC space.

The main objective of this study was to demonstrate the improvement in control performance

and the reduction in energy consumption through controlling temperature, humidity, and veloc-

ity simultaneously rather than independently. The results indicated that this control strategy,

which accounted for control variable interactions among the system, can result in reduced

energy use.

Cumali (1988) adopted the global optimization technique to design optimal control and oper-

ation strategies for building HVAC systems. The optimization problem was formulated based on

the laws of the first principles. The results showed that projected

andfor augmented Lagrange

multiplier methods did not perform well because of the equality constraints used in the problem

formulation, while generalized reduced gradient methods appeared to provide consistent results

if one starts with a reasonable solution.

House et

al. (1991) and House and Smith (1995) described a system approach for optimal

control of building HVAC systems, in which governing equations were derived from the princi-

ples

conservation of mass and energy. The interactive nature of system components, the mul-

tizone building system, and their associated variables were of concern. A nonlinear

programming technique, in which the continuous control variables were discretized in the time

domain

transform the infinite dimensional optimal control problem to a finite dimensional

form, was used to solve the optimal control problem. Using discrete values of the state and con-

trol variables, the cost function was integrated numerically using the trapezoidal rule.

A predictive control policy that utilized a finite-time horizon with end-time constraints was

described by MacArthur and Foslien (1993) and MacArthur and Woessner (1993). The control

system was composed of two distinct components. One was the supervisor, which was used for

sample rate selection and system identification (model development and adaptation). The other

was the controller, which can generate a sequence of control signals to ensure acceptable

servo-regulatory behavior. The control law minimized actuator movement while satisfying both

process and control output constraints imposed at the end of the time horizon. The results

showed that this control method offered adaptability and can easily accommodate system

dynamics and interactions.

Zaheer-uddin and his collaborators have paid considerable efforts on the optimal and

sub-optimal control of HVAC systems in buildings (Zaheer-uddin and

Pate1 1993; Zheng and

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Zaheer-uddin 1996; Zaheer-uddin and Zheng 2000, 2001). These optimal and sub-optimal con-

trol strategies were developed based on the physical models. The simulation results demon-

strated that these optimal and sub-optimal control strategies, in which the multiple control

variables were optimized simultaneously, can improve the system response and operational effi-

ciency. They also demonstrated that multistage optimal control technique is an effective and

useful tool for computing supervisory control profiles for building systems subject to

time-of-day operating schedules.

The system performance and supervisory control for a direct-fired

LiBr absorption chiller

system were investigated by Koeppel et al. (1995) using simulation means. The detailed and

simplified component models were used to predict the system energy and environment perfor-

mance. Simulated annealing as a global optimization algorithm was used to determine the opti-

mal control settings under different control options. The results showed that the optimal

operation schedule for absorption chillers can be determined from the optimal control investiga-

tion under the simulation environment.

The performance of the differential dynamic programming (DDP) technique applied to opti-

mal control of building HVAC systems was studied by Kota et al. (1996). The state equations

utilized to describe the HVAC system were derived from mass and energy conservation princi-

ples. The optimization result was compared with that obtained from a nonlinear programming

(NLP) technique using the SQP method. It was showed that DDP is more efficient compared

with NLP for the example problems, while NLP is more robust and can treat constraints on the

state variables directly.

Henze et al. (1997,2005) devoted great efforts on predictive optimal control of building ther-

mal storage systems using a physical model-based approach. Henze et al. (1997) developed a

predictive optimal controller for thermal energy storage systems, and the performance of this

controller was validated by simulations. This optimal controller minimized operating costs of

the cooling plant over the simulation horizon.

optimal storage charging and discharging

strategy was planned at every time step over a fixed look-ahead time window utilizing newly

available information. The simulation results showed that this optimal controller can achieve a

significant performance benefit over the conventional controls in the presence of complex rate

structures, while requiring only a simple predictor. Henze et al. (2005) demonstrated

model-based predictive optimal control of active and passive building thermal storage inventory

in a test facility in real time using time-of-use differentiated electricity prices without demand

charges. In their study, the building was modeled in the transient systems simulation program,

TRNSYS, while the

Matlab and its optimization toolboxes were used to interface with the build-

ing simulation program. The experimental results showed that the savings associated with pas-

sive building thermal storage inventory were small because the test facility utilized was not an

ideal candidate for the investigated control technology.

Wang and

Jin (2000) presented a supervisory control strategy using a system approach for

VAV air-conditioning systems in which simplified physical models were utilized to predict the

overall system performance, and genetic algorithm (GA) was used to solve the optimization

problem of multiple control variables. It is the first application of

in solving an optimal con-

trol problem formulated using a system approach in HVAC field. The simulation results showed

that this online supervisory control strategy can improve the overall system energy and environ-

ment performance since it took into consideration the system level characteristics and interac-

tions among the system variables.

The International Energy Agency (IEA) research project Annex 17 provided an example to

demonstrate the advance of system simulation in testing and evaluating energy management and

control systems (EMCS) supervisory control strategies for overall building systems, as well as

control strategies implemented in real EMCS by means of emulation (Lebrun and Wang 1993).

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