Elsevier

Solar Energy

Volume 155, October 2017, Pages 561-573
Solar Energy

Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques

https://doi.org/10.1016/j.solener.2017.06.065Get rights and content

Highlights

  • A new method for rooftop PV potential assessment is presented.

  • It allows highly detailed analyses without having to rely on 3D city models.

  • Image recognition and machine learning techniques are applied on aerial images.

  • The method is applied to Freiburg, Germany and validated against a 3D model.

Abstract

The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas provides huge potentials for the mitigation of greenhouse gas emissions. This contribution presents a new method to provide local decision makers with tools to assess the remaining PV potential within their respective communities. It allows highly detailed analyses without having to rely on 3D city models, which are often not available. This is achieved by a combination of publicly available geographical building data and aerial images that are analyzed using image recognition and machine learning approaches. The method also employs sophisticated algorithms for irradiance simulation and power generation that exhibit a higher accuracy than most existing PV potential studies. The method is demonstrated with an application to the city of Freiburg, for which a technical PV electricity generation potential of about 524 GWh/a is identified. A validation with a 3D city model shows that the correct roof azimuth can be determined with an accuracy of about 70% and existing solar installations can be detected with an accuracy of about 90%. This demonstrates that the method can be employed for spatially and temporally detailed PV potential assessments in arbitrary urban areas when only public geographical building data is available instead of exact 3D city model data. Future work will focus on methodological improvements as well as on the integration of the method within an urban energy system modeling framework.

Introduction

There is a worldwide consensus that greenhouse gas emissions should be substantially reduced over the next few decades in order to mitigate climate change (IPCC, 2015). This can only be accomplished through a massive decarbonization of the energy system. One of the most important levers in this endeavor are combinations of energy efficiency measures and renewable energy resources in cities, which will have to play a crucial role in the energy transition (IEA, 2016).

In order to develop local schemes and make informed decisions for the transition to renewable energies, policy makers need to be provided with accurate information on the potential contribution from each of these measures on global as well as on regional and local levels.

The local generation of clean power through PV systems on building roofs, in particular, provides huge potentials that are usually economically viable. Compared to other available options, PV has higher public acceptance, partly because there is less competition for land or other resources.

The assessment of the (remaining) potential for power generation from PV is an important field of study. Methods and tools that enable local decision makers to assess PV potentials in their respective communities are of vital importance for the energy transition. The literature review in Section 2 shows, however, that currently there are no tools available that allow local decision makers to assess these potentials in high detail and accuracy without first having to acquire large amounts of data. With this contribution, the authors intend to address this issue.

Since the requirements for detailed PV potential analyses usually include data that is not publicly available and, especially in smaller municipalities, can not be easily obtained, the objective of this contribution is to present a method for detailed urban PV potential assessment that relies solely on publicly available data and can be applied universally. The authors improve upon existing work as well as their previous publications (e.g. Mainzer et al., 2016) in a number of points:

  • 1.

    high-detailed, bottom-up PV potential analysis in the absence of 3D model data,

  • 2.

    discrete number of actually installable modules instead of just the area,

  • 3.

    consideration of roof objects, e.g. chimneys and windows,

  • 4.

    exact irradiance simulation with high temporal resolution (1/4 hourly),

  • 5.

    detailed, non-linear power generation model with consideration of temperature, module and inverter characteristics,

  • 6.

    consideration of already installed PV modules.

The present literature on the subject is analyzed in Section 2. In Section 3, all steps of the method that was developed are described in detail. Section 4 presents results from an example application of the method to the city of Freiburg, Germany. These results are further analyzed, validated and discussed. In Section 5, the findings are concluded.

Section snippets

Literature review

Several publications have already addressed the problem of identifying PV potentials. The main steps in PV potential estimation methods include the assessment of the available area for PV modules, the simulation of solar irradiance on the tilted module surfaces and the calculation of produced electrical power from the irradiance on these modules. Martín-Chivelet (2016) provides an overview of different methodologies that are employed for each of these steps. As discussed in the following

Methodology

The approach that is used to assess the remaining economic potential in a given region is conducted within nine distinct steps, as shown in Fig. 1.

While some of these steps rely on well-known methods and algorithms, some novel approaches are also presented in this work. These approaches, which are described in steps 2, 4 and 9, are based on the assumption that humans can usually tell the shape, size and suitability of a roof for PV based on its aerial image. Using image recognition techniques,

Results and discussion

The previous section has demonstrated how the method assesses the potential for PV installations in any region by analyzing the roof areas of all buildings and calculating the electricity that could be produced as well as the associated costs.

In this section, the example application of this method to the city of Freiburg, Germany is demonstrated. After showing the aggregated results as well as more detailed results for individual districts (subsection 4.1), the findings are validated by

Conclusion

In this contribution, a new method for the assessment of rooftop PV potentials at the urban level has been presented. This method can be used to conduct PV potential analyses in high detail and in many regions of the world. It uses publicly available geographical building data and aerial images in combination with image recognition techniques to derive the size and orientation of partial roof areas without having to rely on 3D model data.

Compared to existing methods for PV potential assessment,

Acknowledgments

The authors gratefully acknowledge the financial support of the Federal Ministry of Education and Research (BMBF – Germany) for the project Wettbewerb Energieeffiziente Stadt (03SF0415B) and the Nagelschneider Foundation. The authors would also like to thank David Schlund for his contributions to earlier versions of this method.

References (65)

  • T. Huld et al.

    Mapping the performance of PV modules, effects of module type and data averaging

    Sol. Energy

    (2010)
  • J.A. Jakubiec et al.

    A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations

    Sol. Energy

    (2013)
  • J.H. Jo et al.

    A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems

    Renew. Energy

    (2011)
  • S. Killinger et al.

    Projection of power generation between differently-oriented PV systems

    Sol. Energy

    (2016)
  • K. Mainzer et al.

    A high-resolution determination of the technical potential for residential-roof-mounted photovoltaic systems in Germany

    Sol. Energy

    (2014)
  • N. Martín-Chivelet

    Photovoltaic potential and land-use estimation methodology

    Energy

    (2016)
  • G. Mavromatidis et al.

    Evaluation of photovoltaic integration potential in a village

    Sol. Energy

    (2015)
  • H.T. Nguyen et al.

    Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale

    Sol. Energy

    (2012)
  • R. Perez et al.

    Modeling daylight availability and irradiance components from direct and global irradiance

    Sol. Energy

    (1990)
  • L. Romero Rodríguez et al.

    Assessment of the photovoltaic potential at urban level based on 3D city models: a case study and new methodological approach

    Sol. Energy

    (2017)
  • J. Schallenberg-Rodríguez

    Photovoltaic techno-economical potential on roofs in regions and islands: the case of the Canary Islands: methodological review and methodology proposal

    Renew. Sustain. Energy Rev.

    (2013)
  • R. Singh et al.

    Estimation of rooftop solar photovoltaic potential of a city

    Sol. Energy

    (2015)
  • N. Srećković et al.

    Determining roof surfaces suitable for the installation of PV (photovoltaic) systems, based on LiDAR (Light Detection And Ranging) data, pyranometer measurements, and distribution network configuration

    Energy

    (2016)
  • S. Suzuki et al.

    Topological structural analysis of digitized binary images by border following

    Comput. Vision Graph. Image Process.

    (1985)
  • H. Takebayashi et al.

    Study to examine the potential for solar energy utilization based on the relationship between urban morphology and solar radiation gain on building rooftops and wall surfaces

    Sol. Energy

    (2015)
  • I. Theodoridou et al.

    Assessment of retrofitting measures and solar systems’ potential in urban areas using Geographical Information Systems: Application to a Mediterranean city

    Renew. Sustain. Energy Rev.

    (2012)
  • P. Wegertseder et al.

    Combining solar resource mapping and energy system integration methods for realistic valuation of urban solar energy potential

    Sol. Energy

    (2016)
  • H. Wittmann et al.

    Identification of roof areas suited for solar energy conversion systems

    Renew. Energy

    (1997)
  • D. Yang et al.

    Bidirectional irradiance transposition based on the Perez model

    Sol. Energy

    (2014)
  • Bradski, G., 2000. The OpenCV library. Dr. Dobb’s Journal of Software...
  • T. Bührke et al.

    Erneuerbare Energie: Konzepte für die Energiewende

    (2011)
  • Burger, W., Burge, M.J., 2016. Digital image processing: an algorithmic introduction using Java. Texts in computer...
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