Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow

Maoran Sun, Changyu Han, Quan Nie, Jingying Xu, Fan Zhang and Qunshan Zhao

No g8p4f, OSF Preprints from Center for Open Science

Abstract: With buildings consuming nearly 40% of energy in developed countries, it is important to accurately estimate and understand the building energy efficiency in a city. In this research, we propose a deep learning-based multi-source data fusion framework to estimate building energy efficiency. We consider the traditional factors associated with the building energy efficiency from the energy performance certificate for 160,000 properties (30,000 buildings) in Glasgow, UK (e.g., property structural attributes and morphological attributes), as well as the Google Street View (GSV) building façade images as a complement. We compare the performance improvements between our data-fusion framework with traditional morphological attributes and image-only models. The results show that including the building façade images from GSV, the overall model accuracy increases from 79.7% to 86.8%. A further investigation and explanation of the deep learning model are conducted to understand the relationships between building features and building energy efficiency by using Shapley Additive explanations (SHAP). Our research demonstrates the potential of using multi-source data in building energy efficiency prediction to help understand building energy efficiency at the city level to help achieve the net-zero target by 2050.

Date: 2022-05-04
New Economics Papers: this item is included in nep-big and nep-ene
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:g8p4f

DOI: 10.31219/osf.io/g8p4f

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