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Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Author

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  • Kristian Skeie

    (Department of Architecture and Technology, Norwegian University of Science and Technology, Alfred Getz vei 3, 7491 Trondheim, Norway)

  • Arild Gustavsen

    (Department of Architecture and Technology, Norwegian University of Science and Technology, Alfred Getz vei 3, 7491 Trondheim, Norway)

Abstract

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

Suggested Citation

  • Kristian Skeie & Arild Gustavsen, 2021. "Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling," Energies, MDPI, vol. 14(4), pages 1-32, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:802-:d:492520
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    References listed on IDEAS

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    1. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    2. Samuel Van Ackere & Greet Van Eetvelde & David Schillebeeckx & Enrica Papa & Karel Van Wyngene & Lieven Vandevelde, 2015. "Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods," Energies, MDPI, vol. 8(8), pages 1-22, August.
    3. Lukas Lundström & Jan Akander & Jesús Zambrano, 2019. "Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate," Energies, MDPI, vol. 12(3), pages 1-27, February.
    4. Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    5. Lingfors, D. & Bright, J.M. & Engerer, N.A. & Ahlberg, J. & Killinger, S. & Widén, J., 2017. "Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis," Applied Energy, Elsevier, vol. 205(C), pages 1216-1230.
    6. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    7. Daniel Tabas & Jiannong Fang & Fernando Porté-Agel, 2019. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics," Energies, MDPI, vol. 12(7), pages 1-12, April.
    8. Miguel Centeno Brito & Paula Redweik & Cristina Catita & Sara Freitas & Miguel Santos, 2019. "3D Solar Potential in the Urban Environment: A Case Study in Lisbon," Energies, MDPI, vol. 12(18), pages 1-13, September.
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    Cited by:

    1. Razak Olu-Ajayi & Hafiz Alaka & Christian Egwim & Ketty Grishikashvili, 2024. "Comprehensive Analysis of Influencing Factors on Building Energy Performance and Strategic Insights for Sustainable Development: A Systematic Literature Review," Sustainability, MDPI, vol. 16(12), pages 1-27, June.
    2. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2021. "Open Data and Models for Energy and Environment," Energies, MDPI, vol. 14(15), pages 1-2, July.

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