Enhancing HVAC System Efficiency: Development of Virtual Testbed and Evaluation of Equipment Performance
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| Publicado en: | PQDT - Global (2025) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| Resumen: | Air conditioning systems represent a significant share of worldwide energy demand. The International Energy Agency projects that energy demand for space cooling will be more than triple by 2050, driven by urbanization, population growth, and rising global temperatures. Addressing this rising demand is critical to achieving sustainable energy use and meeting global climate goals. Enhancing the performance of cooling systems across all lifecycle stages is essential to meet these objectives. While the design and construction phases play a pivotal role, the operating stage has emerged as particularly significant for achieving long-term energy efficiency. Operational inefficiencies—such as suboptimal control strategies and poor maintenance practices—can exacerbate energy wastage and increase operating costs. Additionally, even systems that are optimally designed and correctly installed are subject to performance degradation over time. To address these challenges, two key areas of focus in operating stage are identified: 1) optimizing operations and controls: refining control algorithms and implementing predictive maintenance strategies can significantly enhance energy efficiency and improve overall system reliability; 2) ensuring equipment performance meets design intent: continuous monitoring of equipment performance is required to detect and mitigate deviations from the original design specifications.Firstly, to optimize operations and controls, this study developed an open-sourced versatile virtual testbed named AlphaDataCenterCooling , which was validated using historical operational data from a real data center cooling plant. This virtual testbed is specifically designed to enhance the overall efficiency and sustainability of data center operations by benchmarking different cooling plant control strategies, thereby facilitating the improvement of energy efficiency and the reduction of carbon emissions. The high-fidelity Modelica model of the cooling plant within the developed virtual testbed supports cross-platform (Python, MATLAB ) co-simulation. After being validated by replicating three months of continuous historical operational conditions, the model demonstrated a mean absolute percentage error (MAPE) in power prediction of only 7.62%. Experiments conducted on the developed testbed checked the AlphaDataCenterCooling’s ease of use and high scalability. The results demonstrated that the developed virtual testbed can effectively serve as a digital twin for data center cooling plants and control systems. This capability allows for the testing of control algorithms without the need for field deployment and shows significant potential for applications in fault diagnosis, predictive maintenance, and the development and testing of advanced control algorithms.Secondly, to ensure the chillers’ performance meets design intent, this study introduces a new metric, the Adaptive Chiller Performance Value (ACPV), and proposes a four-step evaluation procedure for chiller online performance evaluation. ACPV assigns weights based on the proportion of typical operating conditions, identified from the chiller operation data, ensuring that the evaluated performance aligns closely with the system’s specific features. The proposed four step process considers the selection of confidence levels for prediction intervals and the potential impacts of predictive uncertainty during the evaluation. The reliability of the proposed method was validated through a case study conducted on a multi-chiller cooling plant of an operational data center. The complete evaluation process was implemented, and the results indicated that the proposed evaluation framework provides both intuitive and statistically significant insights. Moreover, it can be easily adapted to diverse cooling systems, demonstrating its significance in developing chiller sequencing strategies and facilitating predictive maintenance.Thirdly, to ensure the cooling towers’ performance meets design intent, this study proposes a hybrid physics-informed and data-driven predictive maintenance framework that enables accurate evaluation of cooling tower thermal performance and effective detection of performance degradation using operational data. Maintenance decisions are guided by two key components: (1) characteristic curves derived from Merkel theory, serving as a benchmark for performance evaluation, and (2) model predictive accuracy and prediction interval (PI) reliability metrics, which indicate performance degradation and potential maintenance benefits.The proposed framework was validated using real-world operational data from a data center cooling plant. Compared to existing evaluation methods, it eliminates the need for complex lookup tables, interpolation, and system shutdowns for testing, offering a more streamlined and adaptable approach. By providing a scalable and effective predictive maintenance solution, the framework enhances system reliability and energy efficiency, ensuring more stable and optimized operation.In conclusion, this study emphasizes the operating stage to address key challenges in cooling system efficiency. A virtual testbed was developed to facilitate the testing of control algorithms for cooling plants, and performance evaluation methods were introduced for both chillers and cooling towers. These contributions provide valuable insights into condition monitoring and support predictive maintenance strategies, ultimately advancing energy savings and reducing carbon emissions. |
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| ISBN: | 9798297651623 |
| Fuente: | ProQuest Dissertations & Theses Global |