Model-based building performance evaluation and diagnosis

Pao Yue-kong Library Electronic Theses Database

Model-based building performance evaluation and diagnosis

 

Author: Xu, Xinhua
Title: Model-based building performance evaluation and diagnosis
Degree: Ph.D.
Year: 2005
Subject: Hong Kong Polytechnic University -- Dissertations.
Buildings -- Performance -- Evaluation.
Department: Dept. of Building Services Engineering
Pages: xxi, 263 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1835529
URI: http://theses.lib.polyu.edu.hk/handle/200/116
Abstract: Effective and efficient performance diagnosis and evaluation at the building level is important and practical with easily available measurements at the building level in most contemporary buildings. This thesis presents a systematic methodology for building performance evaluation and diagnosis viewing building system as a whole. This methodology involves the development of a building global performance evaluation tool and practical strategy and approach for practical applications using the evaluation tool. The evaluation tool includes a simplified building energy model and a consolidation AHU model. The simplified building energy model is developed for thermal energy consumption prediction. And the consolidation AHU model is developed to represent all the installed AHU systems for performance prediction of air side systems. With the performance prediction using the tool, a performance signature-based diagnosis strategy is developed for performance diagnosis, and a performance evaluation approach is developed to assess the energy efficiency of alternative strategies for building retrofitting and upgrading. The simplified building energy model is a hybrid model, which is the compounding of physically described simplified models of building envelopes and a partially data driven simplified model of internal mass which possesses physical meanings. The parameters of the simplified model of building envelope (3R2C model) are identified using genetic algorithm (GA) method based on frequency response characteristic analysis. The parameters of the simplified model of internal mass (2R2C) are identified using short-term monitoring operation data based on a GA estimator. The validation shows that the simplified building energy model, which was trained with short-term monitoring operation data, can be extended to dynamically and reliably predict the thermal energy consumption under different operation and weather conditions. To make the modeling process and calibration against monitored data much easier, a consolidation AHU model is presented to represent all the installed AHU systems for airside electricity consumption estimation of all AHU systems. The model was validated in a real building system of good accuracy. Based on the building global performance evaluation tool, a performance signature-based diagnosis strategy is presented for performance diagnosis. Cooling energy consumption is chosen as an important indicator for energy performance diagnosis. The diagnosis process using the strategy shows that the causes resulting in the "measured" cooling energy consumption deviating from the baseline can be identified qualitatively and quantitatively by comparing the observed performance signatures of the "measured" cooling energy consumption with characteristic performance signatures. The diagnosed causes are beneficial for further investigations of building system. A performance evaluation approach, which is also based on the building global performance evaluation tool, is presented to assess the energy efficiency of alternative control strategies for decision-making of building retrofitting and upgrading measures. The approach uses the building global performance evaluation tool for performance prediction. Then, an enthalpy bin method is used to divide predicted performance into a series of enthalpy bins to facilitate evaluation for practical applications. The test in a high rising commercial office building in Hong Kong shows that the approach can effectively evaluate the energy performance of alternative fresh air control strategies at different enthalpy bins. The test also shows that the approach can provide the best combination of alternative basis control strategies for maximum electricity consumption saving. In this study, the optimal fresh air control strategy can save electricity consumption significantly than separate basic control strategies. The approach can provide decision making of system retrofitting to exert the maximum potentials of using fresh air efficiently for electricity consumption savings.

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