Renewable Energy Global Innovations features: Quantifying Rooftop Photovoltaic Solar Energy Potential

Significance Statement

The need to increase the use of non-conventional energy sources in order to reduce the energy demand and greenhouse gas emissions is currently one of the world’s main challenges. Buildings need to become much more energy efficient and the energy demand should be primarily (increasingly) satisfied through renewable energy resources (e.g. solar energy).

One way to increase the production of renewable energy is to promote the solar energy deployment in cities particularly through the use of PV technology. To achieve this aim, a comprehensive assessment of solar PV potential is required. Different approaches have been implemented by researchers in order to study the large-scale solar PV potential of rooftops. Among several methodological approaches, the following have been widely used in order to quantify the available potential of rooftop solar photovoltaic panels. The methods include sampling techniques, statistical methods, aerial images and Geographic Information Systems (GIS) together with LiDAR (Light Detection and Ranging) data.

In a recent paper published in Solar Energy, Dan Assouline, Nahid Mohajeri and Jean-Louis Scartezzini from the Solar Energy and Building Physics Laboratory at Ecole Polytechnique Fédérale de Lausanne in Switzerland used a combination of data-driven methods including a machine-learning algorithm and GIS together with LiDAR data to estimate photovoltaic solar energy potential on building roofs. They investigated the rooftop solar photovoltaic potential of about 1901 communes (the smallest administrative division) in Switzerland. They estimated monthly global horizontal solar radiation (diffuse horizontal, global horizontal and extra-terrestrial horizontal radiation) as well as global tilted solar radiation over rooftops.

A support vector regression (a kernel-based machine learning technique) model was developed and its performance evaluated by the root mean square error (RMSE) and the normalized root mean square (RRMSE). The geographical potential which includes the available roof area and shading factors for the installation of solar photovoltaic is estimated. The authors finally provide an estimation of the technical potential of the rooftop solar photovoltaic energy production per month. Assuming 80% performance ratio and 17% efficiency of solar photovoltaic solar panels, the team found an annual photovoltaic power generation of 17.86 TWh which was equal to 28% of Switzerland’s power consumption as at 2015.

They further found that 15% of the investigated communes provided 53% of electricity used in the country. Maximum values of electricity derived from solar photovoltaic were obtained from large cities like Zurich, Bern and Basel. However, the highest photovoltaic electricity production per capita was found in less populated areas. The total available area for PV installation on the rooftops in the urban areas of Switzerland was found to be 328 km². The total available roof area per capita was also estimated, that is, 41m²/capita. With an annual increase in cell efficiency of the crystalline silicon wafers of 0.3% per year, the PV panel efficiency will increase to 27.2% by 2050. Assuming an increase to 90% of the performance ratio, the rooftop solar PV electricity production for urban areas in Switzerland in 2050 is expected to reach about 32 TWh. This PV electricity production would then provide 37% of the total forecasted (IEA scenario) electricity use in Switzerland in 2050.

About The Author

Dan Assouline is currently a PhD candidate in Swiss Federal Institute of Technology in Lausanne (EPFL) at the Solar Energy and Building Physics Laboratory. He has received a Bachelor’s degree in Applied Mathematics from Lycée Saint Louis (Paris, France), a Master’s degree in Engineering from Ecole Spéciale Des Travaux Publics (Cachan, France) and a Master of Science in Civil and Environmental Engineering from UC Berkeley (California, USA).

His research focuses on the spatio-temporal estimation of large scale renewable energy potential, specifically in urban areas, using Geographic Information Systems and Machine Learning algorithms.

About The Author

Dr Nahid Mohajeri is currently employed as a Postdoctoral Fellow at the Solar Energy and Building Physics Lab (Swiss Federal Institute of Technology in Lausanne, EPFL). She has finished her PhD at University College London (UCL), focusing on the general topic of complex urban systems. Her research interests include statistical modelling of geometric urban patterns, their energy efficiency and ecological impacts.

Her research is also on the physics of urban form and sustainable urban development, as well as impacts of urban form on the renewable energy potentials, for which she uses a variety of techniques. For example, GIS and spatial data analysis, and in particular, the relevant principles from thermodynamics and statistical mechanics/information theory. From September 2017, she will be Assistant Professor at Chalmers University of Technology (Institute of Building Futures Areas of Advance).

About The Author

Professor Dr Jean-Louis Scartezzini is director of the Solar Energy and Building Physics Laboratory (LESO-PB) of the Swiss Federal Institute of Technology in Lausanne (EPFL) and Professor in Building Physics. His scientific research is dedicated to sustainability in the built environment, with a special focus on advanced daylighting systems and green lighting.

He is the author of more than 200 scientific publications and member of several federal commissions and international work groups as well as Associate editor of Solar Energy Journal and the International Journal of Building Physics. He has MSc in Geophysics from University of Lausanne (1981) and MSc in Physical Engineering from EPFL (1980), and PhD in Physics from EPFL (1986).

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Reference

Assouline, D., Mohajeri, N., Scartezzini, J.L. Quantifying Rooftop Photovoltaic Solar Energy Potential: A Machine Learning Approach, Solar Energy 141 (2017) 278–296.

Solar Energy and Building Physics Laboratory (LESO-PB), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

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