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|Title:||Energy performance assessment and diagnosis for information poor buildings|
|Keywords:||Buildings -- Energy conservation.|
Buildings -- Energy consumption.
Hong Kong Polytechnic University -- Dissertations
|Publisher:||The Hong Kong Polytechnic University|
|Abstract:||Enhancing the energy performance of buildings is recognized as one of the most promising methods to reduce the overall energy consumption. Before any building energy saving measure is taken, an energy performance assessment and diagnosis process should be conducted to identify the poor performance areas, the amount of abnormal energy use, and the probable causes behind. Many research and industrial efforts are found in the development and application of the building energy performance assessment and/or diagnosis methods. However, most the energy performance assessment methods often address the overall performance at the building level (e.g., the whole-building benchmarking tools) and most energy performance diagnosis methods often focus on the faults at the component level only (e.g., the FDD (fault detection and diagnosis) methods for chiller, AHU and VAV). Few methods can integrate the performance assessment and the performance diagnosis together and can be applied at different levels (i.e., from the building level to system level and subsystem/component level). A more critical issue is that most existing energy performance assessment and energy performance diagnosis methods/tools are based on the abundant energy performance data from the BAS (building automation system) and/or BMS (building management system) or based on the data from long-term in-situ measurement and conducted manually by well-trained experts. Few methods can be used in information-poor buildings wherein very limited energy and building data are available. This thesis presents a comprehensive energy performance assessment and diagnosis method for enhancing the energy performance of information poor buildings. This method can examine the energy performance at three levels, i.e., from the building level to the system level and the subsystem/component level. The core part of this comprehensive method is the energy performance calculation method which can estimate the energy performance data at different levels using limited energy use data (e.g., the monthly building electricity bill) and few in-situ measurement data of the HVAC system. The estimated performance data mainly include the total energy consumption of the whole building, the individual consumptions of three systems (i.e., HVAC system, "internal-consumers" and "other-consumers"), the building cooling load, and the energy efficiency of the HVAC system and main components. By comparing these estimated performance data with the benchmark data, the energy performance at different level can be assessed and the specific locations of the poor performance can be identified. The principle of the proposed energy performance calculation method is based on two basic energy balance principles, i.e., the electricity consumption balance at the building level and the indicative cooling energy balance between the demand side and the supply side of the HVAC system. These two balances quantitatively describe the most important energy conversion flows between the different but interactive systems in a building. By combing and solving all balance related equations, the energy consumptions of three systems (i.e., HVAC system, "internal-consumers" and "other-consumers") and the performance indicators (i.e., cooling load and energy efficiency) of the HVAC system can be calculated on monthly basis. To avoid the convergence problems and improve the computation efficiency in practice, an error-tolerant optimization algorithm using the trial-and-error method is developed.|
To establish the indicative cooling energy balance of the HVAC system, the building cooling load contributed by various heat gains and the cooling energy supplied by the HVAC system need to be determined. A simplified cooling load calculation method is developed to estimate the monthly cumulated building cooling load. This method is based on the assumption that the total cooling load is equal to the sum of all individual heat gains in the building when the interacting effect of heat gains is ignored. This method, particularly the assumption of ignoring the interacting effect is evaluated by the detailed theoretic assessment and by the case-study analysis using the detailed simulation program. This simplified cooling load calculation method is also validated using the data from two real buildings. To determine the amount of the cooling energy supplied by the HVAC system and examine the energy performance of the HVAC system, two types of HVAC models (i.e., the system-level and component-level models) are developed/selected in this thesis. Using the system-level models, the building performance can be examined at system level by viewing the HVAC system as a whole. Using the component-level models, the building performance can be examined at the component level because the energy consumptions and energy efficiencies of both the HVAC system and main components are calculated using the individual components models (e.g., chiller, pump and fan models). The validation of the developed energy performance calculation method is carried out on two existing buildings in different climates (i.e., a building in Hong Kong and a building in Beijing). The validation results shown that the method can estimate the energy consumptions of three individual systems (i.e., HVAC system, "internal-consumers" and "other-consumers"), and the building cooling load and the energy efficiency of HVAC systems with a satisfactory accuracy in the mechanical cooling period. Although the results in some individual months with free cooling in the building in Beijing are relative poor, the overall results in the whole cooling period are acceptable. Two approaches using different performance benchmarks are presented for assessing the energy performance at different level and identifying the locations of poor energy performance and the causes. One approach is to use performance data from the similar buildings (i.e., generic benchmarks) for benchmarking. If the performance of an assessed building (or a system/component) is lower than the generic benchmark, the building (or system/component) is identified having energy problems. The other diagnostic approach is to use the performance data from the self-reference models (i.e., self-reference benchmarks) for benchmarking. A case study that illustrates how to combine these two approaches for assessing the energy performance and diagnosing the faults of a real building is presented in this thesis as well.
|Description:||xxi, 198 leaves : ill. ; 30 cm.|
PolyU Library Call No.: [THS] LG51 .H577P BSE 2013 Yan
|Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Checked on Aug 13, 2017
Checked on Aug 13, 2017
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