An assessment of the impacts of space heating electrification and future space cooling in the buildings of UK

Zhang, Meng (2024) An assessment of the impacts of space heating electrification and future space cooling in the buildings of UK. PhD thesis, University of Glasgow.

Abstract

This thesis provides a comprehensive examination of the space heating and cooling demand modelling in the UK, linking crucial issues in energy policy with the practicalities of demand forecasting. At its core, the research develops a cohesive methodology that marries the deterministic accuracy of physical models with the predictive agility of ANNs, setting a new standard for energy demand estimation. The research marks a departure from conventional methods, embracing advanced modelling techniques that enhance the prediction and management of energy requirements.

Key findings from this integrated approach reveal that the UK's transition to a decarbonised heating sector will significantly increase electricity demand, highlighting the essential role of energy storage in reducing pressure on the power grid. The work demonstrates that precise and expedient modelling is crucial for providing robust data support to energy policymakers and operators, helping to create quick and well-informed energy strategies. Moreover, the thesis conducts a comprehensive analysis of future cooling demands and the corresponding requirements for diverse cooling technologies. In doing so, it charts potential scenarios for cooling equipment needs, preparing stakeholders for shifts in consumer demand patterns. Additionally, the thesis delves into the performance of neural networks within the context of energy prediction, illustrating their efficacy and potential to transform the landscape of energy demand forecasting.

Through this investigation, the thesis contributes to a nuanced understanding of energy demand, aiding the development of energy policies and infrastructures that are not only sustainable but also adaptive to the pressing demands of climate change and technological evolution. This work lays a foundational blueprint for future research initiatives aimed at optimising energy systems and underscores the importance of integrating varied modelling techniques to achieve greater accuracy and efficiency in energy demand forecasting.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > T Technology (General); T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Yang, Dr. Jin and Yu, Professor Zhibin
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84559
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 17 Sep 2024 08:00
• Last Modified: 17 Sep 2024 10:24
• Thesis DOI: 10.5525/gla.thesis.84559
• URI: https://theses.gla.ac.uk/id/eprint/84559

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