Accessibility navigation


Modelling heating and cooling energy demand for building stock using a hybrid approach

Li, X. and Yao, R. (2021) Modelling heating and cooling energy demand for building stock using a hybrid approach. Energy and Buildings, 235. 110740. ISSN 0378-7788

[img] Text - Accepted Version
· Restricted to Repository staff only until 14 January 2022.

1MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.enbuild.2021.110740

Abstract/Summary

The building sector accounts for 30% of final energy consumption and 28% of global energy-related carbon dioxide emissions, with space heating and cooling consuming a large share of total buildings’ energy consumption. Building stock modelling for space heating and cooling energy prediction provides critical insights on the stock energy consumption and aid the building retrofit policy-making process with the evaluation of the energy-saving potential. By combining the physical modelling approach and data-driven approach, a hybrid approach is applicable for modelling the heating and cooling energy consumption of the building stock, including both residential buildings and non-residential buildings. Within this framework, the Urban Modelling Interface (UMI) tool has been used for physical modelling to generate heating and cooling energy use intensity. Then, ten different machine learning models, including Gaussian radial basis function kernel support vector regression, linear kernel support vector regression, polynomial kernel support vector regression, random forests, extreme gradient boosting, ordinary least-squares linear regression, ridge regression, least absolute shrinkage and selection operator, elastic net and artificial neural network, have been applied to predict heating and cooling energy use intensity (EUI). The approach has been demonstrated using a case study in Chongqing, China. The results show that machine learning models can achieve accurate building heating and cooling EUI prediction, with the polynomial kernel support vector regression showing the best accuracy at the level of a single building, and the Gaussian radial basis function kernel support vector regression performing the best at the stock level. Machine learning models generated by proposed hybrid approach not only provide quickly prediction of building space heating and cooling energy consumption at the stock level, but also support building retrofit decision makings by evaluate energy saving potential of various retrofit options.

Item Type:Article
Refereed:Yes
Divisions:Science > School of the Built Environment > Construction Management and Engineering > Innovative and Sustainable Technologies
ID Code:98368
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation