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Machine learning for healthy building design


Project Description

The 2003 heatwave caused over 2000 heat-related deaths in the UK and more than 30,000 across Europe. With climate change, heatwaves are becoming more intense, frequent and longer duration in the UK. The heat-related mortality occurs most in buildings as people spend 90% of their time indoors especially for the vulnerable population such as the elderly and children. It is essential to predict indoor overheating risk in advance so that proactive action could be made to fight against heatwaves. However, current prediction approaches based on time series regression or artificial neural networks cannot provide accurate and reliable forecast of indoor temperature under heat wave attack, especially over long-term forecasting horizons. This project is devoted to developing a more accurate and reliable forecasting approach by employing the recently developed advanced machine learning techniques.

The main tasks of the project include:
1) Establish a polynomial model (ARX, ARMAX, output-error, or Box-Jenkins) to link past observed data and the future data to be predicted.
2) Develop an algorithm based on advanced machine learning that can identify and update the model parameters online.
3) Verify the developed algorithm through simulation and numerical experiment.

Applicants are expected to have a very good bachelor’s or master’s degree in the subjects of mechanical engineering, building service engineering, environmental engineering, control engineering or related subject with research interests in the application of artificial intelligence techniques to building-related research. Basic coding skills are essential and some knowledge on mathematical modelling/machine learning is desirable. The PhD student will be supervised by Dr Zhiwen Luo (Building environmental engineering at the University of Reading) and Dr Zhan Shu (Control engineering at the University of Southampton)

Funding Notes

First-class or 2.1 (Hons) degree, or Masters degree (or equivalent) in an appropriate subject

How good is research at University of Reading in Architecture, Built Environment and Planning?

FTE Category A staff submitted: 34.90

Research output data provided by the Research Excellence Framework (REF)

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