Modelling on-domestic buildings energy performance using machine learning methods, a case study of the UK

In the UK, only 7% of non-domestic buildings are newly built, whilst this sector generates 20% of total gas emission. Consequently, the government has setregulations to decrease the amount of energy take-up by buildings. It is apparent from the seminal literature that deep energy retrofit is the primary solution to achieve that goal. Due to the size and complexity of non-domestic buildings, finding optimum plans is cumbersome. To that end, artificial intelligence has been employed to assist this decision-making procedure, yet limited to high time-complexity of energy simulations. Surrogate modelling seems a promising alternative for simulation software, developing accurate energy prediction models requires an understanding of the building physics and a vision on the use of data-driven models. This study evaluated the accuracy and time complexity of most popular Machine Learning (ML) methods in the buildings energy efficiency estimation. It established an approach based on evolutionary optimisation to reach the highest potential of MLs in predicting buildings energy performance. It then developed an energy performance prediction model for the UK non-domestic buildings with the aid of ML techniques. The ML model amid at supporting multi-objective optimisation of energy retrofit planning by accelerating energy performance computation. The study laid out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It outlines a framework to represent the building records as a set of features in away that all alterations produced by applying retrofit technologies can be captured by the model to generate accurate energy ratings. The model provides a reliable tool to explore a large space of the available building materials and technologies for evaluating thousands of buildings going under retrofit to fulfil the energy policy targets and enables building analysts to explore the expanding solution space meaningfully.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral-pg

Grantor dc:publisher.institution

University of Strathclyde

Year dc:date.issued

2020

Author dc:creator

• Seyedzadeh, Saleh

Advisors dc:contributor.advisor

• Glesk, Ivan
• Rahimian, Farzad Pour

DOI

10.48730/6qvm-4z80

Identifier

T15525

Author Identifier

201674745

OAI identifier oai:identifier

oai:strathclyde:05741r75d