Case Studies > Climate Analytics
Climate Change Adaptation Study of a Higher Education Building in the UK
| About | Use of future weather files in building performance evaluations when designing for resilience in climate change scenarios. |  |
| By | Eleni Davidson, University College London, London, UK | |
| Location | Central London | |
| Data used | Real weather data for model calibration TMY files for 2030, 2050, 2080 | |
| Category | Existing Building Retrofit, Future Modelling | |
| Highlights |
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This case study explores the performance of an existing university building under future climate scenarios. The work presented here focuses on the use of weather files and forms a part of a larger climate adaptation study of the UK higher education sector being undertaken at University College London. The figure below shows the image of the case study building (from Google Earth) and its model created in DesignBuilder.
In this case study, a calibrated model is developed, and the energy loads and thermal performance of the building are assessed under a range of climate change projections for 2030, 2050 and 2080 future weather years. After briefly describing the development of the calibrated model, the study demonstrates the use of future climate weather files. In the last section, the larger context of the overall research project is explained.
Weather Data Used in Model Calibration
Using the CIBSE TM63 calibration process, the calibrated model was developed to meet the criteria defined by ASHRAE Guideline 14 and IPMVP. A key factor in the development of calibrated models is the use of real weather data. In this case, actual weather data from the London WEA Centre Station for the year 2018 was used, aligning with the monitoring period of the building. Further evidence-based finetuning of other model input parameters resulted in a calibrated energy model.
Using appropriate weather data, operational assumptions, loads and system configuration, the building’s monthly energy use profiles were calculated and compared with the equivalent measured profiles. The figure above summarises the results, comparing the calibrated model projections for heat and electricity consumption with actual measured values. ASHRAE Guideline 14 requires monthly CVRMSE (Coefficient of Variation of Root Mean Square Error) to be less than 15% and NMBE (Nominal Mean Bias Error) to be ±5%. The calibrated model has monthly heat use errors of CVRMSE = 12% and NMBE = +3%. For the monthly electricity use, the error metrics are CVRMSE = 7% and NMBE =+3%, so the calibrated model is well within Guideline 14 requirements.
Future Climate Modelling using the Calibrated Model
After the calibrated model had been developed and checked, its performance was assessed under future climate scenarios. Three future weather files were created using DesignBuilder Climate Analytics based on the London WEA Centre Typical year (TMY) weather file and the Hadley Centre Future Climate model:
- Near Term: Hadley Centre HADCM3_B1A_2010-2039 (commonly referred to as 2030 scenario)
- Medium Term: Hadley Centre HADCM3_B1A_2040-2069 (commonly referred to as 2050 scenario)
- Long Term: Hadley Centre HADCM3_B1A_2070-2099 (commonly referred to as 2080 scenario)
These weather files follow the B1 socio-economic storyline for the planet’s future, one of the equally probable future world directions, as explained in the IPCC Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios. The B1 storyline assumes a convergent world with a global population that peaks in mid-century and declines thereafter but with rapid changes in economic structures toward a service and information economy, with reductions in consumption and the introduction of clean and resource-efficient technologies.
Comparing the dry bulb temperatures for the 2018 weather file, used in the baseline and the three future climate projections, it is evident that, for this location, over the coming years, the summers will get significantly warmer than the winters. The figure below shows that over the years, a typical summer week is expected to have a warming of about 5°C whereas during a typical winter week, the warming will only be about 2°C.
The impact of this changing climate pattern can also be observed in the case study buildings building’s energy and environmental performance. The graph shows the comparative energy use for the building, the current performance and the three future scenarios. They provide an indication of how the performance of the building will change over time.
In future scenarios, the total energy use of the building is expected to increase by less than 10%. However, the end-use breakdown shows that the proportion of energy used for heating and cooling changes significantly. Heating energy use is reduced by 25% whereas cooling energy use is increased by more than 60%.
This demonstration shows the role that future weather data can play in exploring building resilience in climate change scenarios. Using this and other similar analyses, further adaptation and mitigation measures were also explored as a part of a wider research project, as explained in the next section.
Summary of Further Research
This case study sits within a larger research project focusing on climate change adaptation strategies, using multiple buildings, across the UK higher education sector. In the overall research project, these models are being used for a detailed assessment of building performance. Iterative changes to the buildings’ design are implemented with the intention of counteracting increasing cooling loads and achieving a reduction in overall operational energy use. The embodied carbon and upfront costs associated with these hypothetical refurbishment strategies are also investigated. The aim is to better understand the trade-offs that arise between embodied vs. operational carbon, and upfront vs. ongoing costs when designing for future climates. A set of Pareto-optimal solutions that achieve minimal life-cycle carbon and life-cycle cost are identified.
The outputs from this work include a climate change adaptation decision-support framework that optimises building design strategies whilst incorporating uncertainty relating to future climate projections. The proposed framework operates as a feedback process, evaluating the impact of modelling assumptions relating to energy system decarbonisation on framework outputs, such as operational energy decarbonisation rates and variable fuel prices. The purpose is to provide a mechanism by which the sensitivity of optimal design strategies to probabilistic climate projections and decarbonisation predictions can be observed.
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