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Next generation heat pump system evaluation methodologies.

Author(s) : WAN H.

Type of monograph: Thesis

Summary

Energy consumption of heat pump (HP) systems plays a significant role in the global residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to fully reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests no longer than one year period. Only limited studies conducted the system performance tests over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming due to the increased demands but also can be a part of the solution by replacing fossil fuel burning heating systems. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct greenhouse gas (GHG) emissions from the refrigerant leakage, indirect GHG emissions from the power consumption and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Climate Performance (LCCP) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) enhanced life cycle climate performance (En-LCCP) analysis. This study made following observations: First, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system's dynamic performance. However, the complicated structure of the networks, numerous parameters needing optimization, and longer training time are the main challenges for these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the LCCP results closer to reality and provided alternative methods for determining LCCP input parameters with limited-data cases. Future work could be studying the uncertainty within the deep learning networks and finding a general process for modeling settings. People may also develop a multi-objective optimization model for HP system design while considering both the LCCP and cost.

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Pages: 238 p.

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Details

  • Original title: Next generation heat pump system evaluation methodologies.
  • Record ID : 30030794
  • Languages: English
  • Subject: Technology
  • Publication: University of maryland - College park - United states
  • Publication date: 2021

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