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Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0001-6586-4932
Australian National University.ORCID iD: 0000-0002-9465-3453
Australian National University.ORCID iD: 0000-0003-3966-3058
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.
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2017 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 205, p. 1216-1230Article in journal (Refereed) Published
Abstract [en]

LiDAR (Light Detection and Ranging) data have recently gained popularity for use in solar resource assessment and solar photovoltaics (PV) suitability studies in the built environment due to robustness at identifying building orientation, roof tilt and shading. There is a disparity in the geographic coverage of low- and high-resolution LiDAR data (LL and LH, respectively) between rural and urban locations, as the cost of the latter is often not justified for rural areas where high PV penetrations often pose the greatest impact on the electricity distribution network. There is a need for a comparison of the different resolutions to assess capability of LL. In this study, we evaluated and improved upon a previously reported methodology that derives roof types from a LiDAR-derived, low-resolution Digital Surface Model (DSM) with a co-classing routine. Key improvements to the methodology include: co-classing routine adapted for raw LiDAR data, applicability to differing building type distribution in study area, building height and symmetry considerations, a vector-based shading analysis of building surfaces and the addition of solar resource assessment capability.

Based on the performance of different LiDAR resolutions within the developed model, a comparison between LL (0.5-1 pts/m(2)) and LH (6-8 pts/m(2)) LiDAR data was applied; LH can confidently be used to evaluate the applicability of LL, due to its significantly higher point density and therefore accuracy. We find that the co-classing methodology works satisfactory for LL for all types of building distributions. Roof-type identification errors from incorrect co-classing were rare (< 1%) with LL. Co-classing buildings using LL improves accuracy of roof-type identification in areas with homogeneous distribution of buildings, here from 78% to 86% in accuracy. Contrastingly, co-classing accuracy using LH is marginally reduced for all building distributions from 94.8% to 94.4%. We adapt the Hay and Davies solar transposition model to include shading. The shading analysis demonstrates similarity of results between LL and LH. We find that the proposed methodology can confidently be used for solar resource assessments on buildings when only LiDAR data of low-resolution (< 1 pts/m(2)) is available.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 205, p. 1216-1230
Keyword [en]
LiDAR, Solar resource assessment, Shading, Building classification, Low-resolution, High-resolution
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
URN: urn:nbn:se:uu:diva-332226DOI: 10.1016/j.apenergy.2017.08.045ISI: 000414817100098OAI: oai:DiVA.org:uu-332226DiVA, id: diva2:1152562
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2018-02-20Bibliographically approved
In thesis
1. Solar Variability Assessment in the Built Environment: Model Development and Application to Grid Integration
Open this publication in new window or tab >>Solar Variability Assessment in the Built Environment: Model Development and Application to Grid Integration
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Variationer i Solelgenerering i den Byggda Miljön : Modellutveckling och Integration i Elnätet
Abstract [en]

During the 21st century there has been a rapid increase in grid-connected photovoltaic (PV) capacity globally, due to falling system component prices and introduction of various economic incentives. To a large extent, PV systems are installed on buildings, which means they are widely distributed and located close to the power consumer, in contrast to conventional power plants. The intermittency of solar irradiance poses challenges to the integration of PV, which may be mitigated if properly assessing the solar resource. In this thesis, methods have been developed for solar variability and resource assessment in the built environment on both national and local level, and have been applied to grid integration studies. On national level, a method based on building statistics was developed that reproduces the hourly PV power generation in Sweden with high accuracy; correlation between simulated and real power generation for 2012 and 2013 were 0.97 and 0.99, respectively. The model was applied in scenarios of high penetration of intermittent renewable energy (IRE) in the Nordic synchronous power system, in combination with similar models for wind, wave and tidal power. A mix of the IRE resources was sought to minimise the variability in net load (i.e., load minus IRE, nuclear and thermal power). The study showed that a fully renewable Nordic power system is possible if hydropower operation is properly planned for. However, the contribution from PV power would only be 2-3% of the total power demand, due to strong diurnal and seasonal variability. On local level, a model-driven solar resource assessment method was developed based on low-resolution LiDAR (Light Detection and Ranging) data. It was shown to improve the representation of buildings, i.e., roof shape, tilt and azimuth, over raster-based methods, i.e., digital surface models (DSM), which use the same LiDAR data. Furthermore, the new method can provide time-resolved data in contrast to traditional solar maps, and can thus be used as a powerful tool when studying the integration of high penetrations of PV in the distribution grid. In conclusion, the developed methods fill important gaps in our ability to plan for a fully renewable power system.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 92
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1598
Keyword
Solar Variability, Photovoltaics, Grid Integration, Distributed Generation, LiDAR, GIS
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-332714 (URN)978-91-513-0149-5 (ISBN)
Public defence
2017-12-21, Häggsalen, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2017-11-29 Created: 2017-11-01 Last updated: 2018-03-07

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Lingfors, DavidWidén, Joakim

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