Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building Environment and Energy Engineering | en_US |
| dc.contributor.advisor | Wang, Shengwei (BEEE) | en_US |
| dc.creator | Sun, Shaobo | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12325 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Measurement uncertainty quantification and probability-based control for building central cooling and air conditioning systems | en_US |
| dcterms.abstract | Measurements are of great importance to health monitoring, performance evaluation and online control of heating, ventilation, and air conditioning (HVAC) systems. The accuracy of the measurements used, to a certain extent, determines the reliability of the decision-making. Uncertainties inevitably exist in measurements. The uncertainties outside the normal/acceptable range may lead to significant negative impacts on the performance of HVAC systems. Existing studies tend to use indirect methods to reduce the impacts of measurement uncertainties on HVAC systems, such as control optimization, and sensor fault detection. Though these methods performed well in their respective application scenarios, their flexibility and generalization ability are poor. An effective and direct measurement uncertainty quantification method is urgently needed for HVAC systems, and online corrections of measurements with unacceptable uncertainties also need to be done for improving the reliability of HVAC systems and extending the service life of measuring instruments. | en_US |
| dcterms.abstract | This PhD study proposes a measurement uncertainty quantification framework for HVAC systems using Bayesian inference and Markov chain Monte Carlo sampling methods. | en_US |
| dcterms.abstract | Based on the framework, two measurement uncertainty quantification methods are developed, one based on physical models, and another one based on data-driven models. The physical model-based measurement uncertainty quantification method is tested and validated systematically on virtual water-cooled multiple chiller plants. The energy and mass balance models are established. The test results show that the measurement uncertainties (including the systematic uncertainty and random uncertainty) of chilled water and cooling water flow rates can be quantified successfully using the developed physical model-based method. The data-driven model-based measurement uncertainty quantification method is tested and validated systematically on an actual air-cooled chiller. A multiple quadratic non-linear regression model is established. The test results show that the developed data-driven model-based method can effectively quantify both the systematic and random uncertainties of chilled water flow rates, and the relative errors are within 10.00%. The two developed methods show satisfactory performance in quantifying measurement uncertainties of HVAC systems. | en_US |
| dcterms.abstract | Based on the physical model-based measurement uncertainty quantification method, a probability-based chiller sequencing control strategy is proposed. The measured chilled water flow rate is corrected online, and the distribution of real-time cooling load can further be obtained. The control decisions are made according to the probability that the cooling load is distributed in different intervals, and the risk of decision-making can also be quantified. The results show that the root-mean-square error of cooling loads is reduced significantly by about 79% after the correction of chilled water flow rates. Compared with the conventional cooling load-based chiller sequencing control, the impacts of both positive and negative uncertainties on system operation can be reduced significantly when using the proposed control strategy. | en_US |
| dcterms.abstract | Based on the data-driven model-based measurement uncertainty quantification method, a fresh air control optimization strategy for air handling units is proposed. The impacts of humidity measurement uncertainties on enthalpy-based fresh air control are evaluated. A multiple quadratic non-linear regression model is established to address the uncertainties of relative humidity measurements and optimize the fresh air control. The relative humidity values of fresh air and return air are corrected and used to calculate their enthalpies for control decision making. The proposed strategy is tested on a virtual platform. The test results show that the proposed fresh air control optimization strategy can significantly reduce the impacts of uncertainties of relative humidity measurements on system operation. Compared with the energy consumption before optimization, the energy consumption of the air handling unit is reduced by 1.02% - 24.58% after optimization. | en_US |
| dcterms.extent | xxii, 162 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2023 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Heating | en_US |
| dcterms.LCSH | Ventilation | en_US |
| dcterms.LCSH | Air conditioning | en_US |
| dcterms.LCSH | Buildings -- Energy conservation | en_US |
| dcterms.LCSH | Uncertainty | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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