Mapping solar PV potential for existing buildings stocks in the UK by deep learning of satellite and aerial images data

About the Project

Project Abstract

We are seeking an ambitious PhD candidate to join a cutting-edge research project aiming to map the solar photovoltaic (PV) potential for existing building stocks in the UK through the application of deep learning on satellite and aerial image data. The anticipated research outcomes will provide valuable insights for the construction industry to strategically promote solar PV installations across the UK. The findings will significantly contribute to enhancing sustainability, aligning with the UK's goal of achieving net-zero carbon targets by 2050.

Solar PV technology stands out as one of the most promising renewable energy technologies in the global energy markets. Over the past two decades, the installation capacity of solar PV systems in the UK has experienced rapid growth. As a representative active solution for sustainable buildings and infrastructure, it holds great potential to significantly reduce operational carbon emissions in buildings. Simultaneously, it contributes by generating renewable electricity, easing the loads on the state grid. However, a notable research gap exists regarding the extent to which rooftop solar PV panels can be installed on existing building stocks in specific regions of the UK for renewable electricity generation and the decarbonisation of building energy use. Research inquiry of the proposed project includes assessing the available space for rooftop solar PV installations and mapping regional PV capacity potential across the UK.

For this purpose, it is interesting to apply Artificial Intelligence (AI) techniques to estimate the regional rooftop areas that are bare without solar PV installation. AI, currently a focal point across diverse industries, notably in the construction sector, drives digital transformation, enhancing efficiency, productivity, and quality assurance from the planning stage through construction to post-construction stages [1]. One of the advanced AI techniques, deep learning, will be employed to map the regional rooftop solar PV potential for early construction planning in construction industry.

The candidate will be expected to collect the GIS (Geographic Information Systems) data and relevant information, as well as use deep learning and semantic segmentation techniques to identify the regional potential of rooftop areas for existing building stocks from satellite and aerial images [2]. Then, regional potential of rooftop PV installation capacity across the UK will be estimated statistically and mapped using GIS analysis software (e. g. QGIS, or ArcGIS). The research results will establish a robust foundation for strategically promoting rooftop PV installation in the UK construction industry, fostering long-term sustainable development. The findings will not only inform the regional potential of PV installation capacity for renewable electricity generation, but also draw attention from government, stakeholders, and policymakers in relevant industry. This, in turn, can guide the formulation of effective incentive policies for solar PV installation, driving economic growth in both upstream and downstream production chains of solar PV systems.

The idea candidate should have obtained an Honours Degree classified above 2:1 (or equivalent) in renewable energy, architectural engineering, geographic information systems, computer science or other relevant engineering subject areas. Research experience in deep learning, big data analysis, programming language (e.g., Python, MATLAB) will be a plus in the application.

Funding Notes

there is no funding for this project

References

[1] Shanaka Kristombu Baduge, Sadeep Thilakarathna, Jude Shalitha Perera, Mehrdad Arashpour, Pejman Sharafi, Bertrand Teodosio, Ankit Shringi, Priyan Mendis (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction 141: 104440.
[2] Hongzhi Mao, Xie Chen, Yongqiang Luo, Jie Deng, Zhiyong Tian, Jinhua Yu, Yimin Xiao, Jianhua Fan (2023). Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images. Renewable and Sustainable Energy Reviews 179: 113276.