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|Title: ||Evolutionary synthesis of HVAC system configurations : experimental results|
|Authors: ||Wright, Jonathan A.|
|Issue Date: ||2008|
|Publisher: ||© American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.|
|Citation: ||WRIGHT, J. and ZHANG, Y., 2008. Evolutionary synthesis of HVAC system configurations : experimental results. HVAC&R Research, 14 (1), pp. 57-72|
|Abstract: ||The aim of this research was to investigate the synthesis of novel heating, ventilating, and air
conditioning (HVAC) system configurations using model-based optimization methods (Wright et
al. 2008). This paper describes the experimental results for the optimization of a two-zone HVAC
system of a building located in a continental climate. The goal of the optimization was to find a
feasible system design that operated with the minimum system capacity at each load condition.
The optimization method used in this research is based on a Genetic Algorithm search
method. The robustness of the optimization was examined through the consistency of the design
solutions found from multiple runs of the algorithm (each run being subject to different initial
conditions). The results indicate that given two runs of the algorithm, there was a high probability
of finding a system design that has a performance comparable to existing system configurations.
Given eight runs of the algorithm, it is probable that the best system found would have a
performance that exceeded that of existing system configurations. However, approximately one
third of all optimization runs would converge onto an infeasible system configuration, the elimination
of this characteristic being the subject of future research.
The optimality of the synthesized systems was judged in comparison to the performance of
three benchmark systems and by comparing the system capacity to the minimum possible at a
given load condition. The best of the synthesized systems had a performance that exceeded that
of the conventional benchmark systems and that was comparable to that of a conceptually optimum
system configuration. The system capacity was also close to the minimum possible capacity
and as such was judged to be a near-optimum system configuration for the example building.
It can be concluded that the optimization approach is able to synthesize near-optimum system
configurations that have a performance equal to or better than existing system configurations.
The algorithm, however, requires multiple runs in order to find reliable solutions, a fact
that should be addressed in future research. The current algorithm, however, represents a significant
step toward the design of software systems that are able to synthesize new and optimum
HVAC system configurations.|
|Description: ||This is a journal article [© American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org)]. Reprinted by permission from HVAC&R Research, Vol. 14, Part 1. Additional reproduction, distribution, or transmission in either print or digital form is not permitted without ASHRAE’s prior written permission. It is also available at: www.ashrae.org/hvacr-research|
|Appears in Collections:||Published Articles (Civil and Building Engineering)|
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