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|Title: ||An intelligent approach to access building occupancy for cooling load prediction|
|Other Titles: ||Ji lou yu shi yong lü jin xing leng fu he yu ce de zhi neng fang fa|
|Authors: ||Kwok, Sai Kwong (郭世剛)|
|Department: ||Department of Building and Construction|
|Degree: ||Engineering Doctorate|
|Issue Date: ||2010|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Air conditioning -- Efficiency.|
Cooling load -- Measurement.
Buildings -- Energy conservation.
|Notes: ||CityU Call Number: TH7687.5 .K85 2010|
xvii, 219,  leaves : ill. 30 cm.
Thesis (Eng.D.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves 191-213)
|Abstract: ||Building cooling load prediction is one of the key factors in the success of
energy-saving measures. Many computational models available in the industry today
have been developed from either forward or inverse modeling approaches. However,
most of these models require extensive computer resources and involve lengthy
computation. This thesis discusses the use of the multi-layer perceptron (MLP)
model - an artificial neural network (ANN) model widely adopted in engineering
applications that offers unique characteristics such as adaptability, nonlinearity, and
arbitrary function mapping ability - to predict the cooling load of a building.
Although it is common knowledge that the presence and activity of building
occupants have a significant impact on the required cooling load of buildings,
practices currently adopted in modeling the presence and activity of people in
buildings do not reflect the complexity of the impact occupants have on building
cooling load. In contrast to previous ANN models, most of which employ a fixed
schedule or historic load data to represent building occupancy in simulating building
cooling load, this thesis introduces a stochastic model of occupants’ presence and
activity and uses it as one of the input parameters to mimic building cooling load.
The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from two existing grade A mega
office buildings in Hong Kong with tenants including many multinational financial
companies that require 24-hour air conditioning seven days a week. The dynamic
changes that occur in the occupancy of these buildings therefore make it very difficult
to forecast building cooling load by means of a fixed time schedule. The paper also
discusses the practical difficulties and limitations encountered in obtaining
building-related data, particularly those faced in acquiring dynamic data.
The performance of simulation results for the two mega office buildings
examined demonstrate that building occupancy data play a critical role in building
cooling load prediction and that their use significantly improves the predictive
accuracy of cooling load models.
Keywords: Artificial Neural Network, Cooling Load, Building Occupancy.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3947676|
|Appears in Collections:||BC - Doctor of Engineering|
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