Profile

Ying Yan (Henna) Li

PhD Student
UCL
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Henna completed a BEng in mechanical engineering in The University of Hong Kong and holds an MSc in Built Environment: Environmental Design and Engineering from University College London. The education background enabled her to have a comprehensive understanding of the mechanisms regarding the built environment and sustainability. She is interested in evaluating energy performance and energy benchmarking of buildings. She is also keen on exploring ways to stimulate decarbonisation of the built environment.
Enhancing Energy Benchmarking and Decarbonisation of NHS Hospitals in England: Insights from General Acute Hospitals

The healthcare sector in the UK made up approximately 5% of the country’s overall emissions and is now committed to achieve net zero and provide sustainable healthcare services. Hospitals, as complex buildings with high energy use intensities and diverse energy use patterns, are critical to this transition. To enhance energy efficiency and support decarbonisation within the National Health Service (NHS) hospital estate, a comprehensive understanding regarding operational energy use of hospitals is essential. Existing studies have identified potential determinants of energy use of hospitals such as building characteristics, occupancy, services provided and location. Energy benchmarking plays a significant role in promoting building rating systems and analyses of energy saving scenarios. Energy benchmarks indicating typical energy use intensities of four hospital typologies are developed by CIBSE. Recent research aims to improve the accuracy of hospital energy benchmarking through energy load predictions, hospital categorisation, etc. However, there is a lack of studies exploring the influences of various factors on hospitals’ operational energy use and how these insights can be incorporated into energy benchmarking process of the hospital estate with data of high granularity.

This PhD aims to address this research gap by improving understanding of factors influencing energy use in hospitals, including site and building characteristics, spatial variation and temporal variations, and exploring the possibility of improving fairness and accuracy of energy benchmarking using large-scale, high-resolution data. It aims to enhance the effectiveness of operational energy performance evaluations and explore decarbonisation pathways in UK’s hospital sector. The proposed methodology involves analysing large set of hospital contextual information and energy consumption data at various resolutions, including half-hourly datasets, with statistical and machine learning approaches. It is expected that findings from this research may contribute to broader effort to enhance operational energy benchmarking of other complex building types and promoting energy efficiency and sustainability of the built environment.
Project in partnership with: CIBSE, NHS England