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Future Weather Data for Dynamic Building Energy Simulations: Overview of Available Data and Presentation of Newly Derived Data for Belgium

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Energy Sustainability in Built and Urban Environments

Part of the book series: Energy, Environment, and Sustainability ((ENENSU))

Abstract

As buildings have a relatively long life span, it is important to consider climate change in energy performance modelling. Good quality weather data are needed to obtain accurate results. This chapter discusses widely used methods to predict future weather data (dynamical downscaling, stochastic weather generators and morphing) and provides an overview of available weather datasets (multi-year, typical years, extreme years and representative years) for building simulations. A Flemish office building is used for a comparative analysis of the estimated heating and cooling load making use of 1-year weather files (typical and extreme future climate conditions) derived from a recently developed convection-permitting climate model for Belgium. Climate models and weather generators are identified as the most preferred for the estimation of the average energy consumption and thermal comfort in average and extreme situations. Climate models have the advantage to better represent extreme weather events and climate differences due to territorial settings, while weather generators can generate multiple climate realizations. A combination of a typical year with an extreme cold and extreme warm year was found to result in an overall good representation of the energy need for heating and cooling in average and extreme weather conditions. Further, the influence of the methodological choices to extract 1-year weather files (typical or extreme years) from the 30-year climate data is highlighted as different results were obtained when different meteorological variables were considered for the creation of the 1-year files.

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Notes

  1. 1.

    Hadley Centre Coupled Model, version 3.

  2. 2.

    Medium-high emission scenario. One of the scenarios before the development of RCP scenarios to be used in GCMs for climate change.

  3. 3.

    Defined as “the cumulative squared hourly difference between the outdoor dry-bulb temperature and the adaptive thermal comfort temperature” (CIBSE 2014: p. 1).

  4. 4.

    EC-Earth is a global climate model developed by a European consortium (Hazeleger et al. 2010).

  5. 5.

    Consortium for small scale modelling in Climate mode is a regional climate model (Vanden Broucke 2017).

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Acknowledgements

This chapter is part of an SBO Ph.D. fellowship ‘Towards future-proof buildings in Flanders: Climate and Life Cycle modelling for resilient office buildings’ (1S97418 N) funded by Research Foundation Flanders (FWO).

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Authors and Affiliations

  1. Faculty of Engineering Science, Department of Architecture, KU Leuven, Louvain, Belgium

    Delphine Ramon, Karen Allacker & Frank De Troyer

  2. Faculty of Science, Department of Earth and Environmental Sciences, KU Leuven, Louvain, Belgium

    Nicole P. M. van Lipzig

  3. Faculty of Bioscience Engineering, Department of Forest and Water Management, UGent, Ghent, Belgium

    Hendrik Wouters

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Correspondence to Delphine Ramon .

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Editors and Affiliations

  1. Department of Electrical Engineering, KU Leuven (Catholic University Leuven), Ghent, Belgium

    Emilia Motoasca

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Avinash Kumar Agarwal

  3. Department of Civil Engineering, KU Leuven (Catholic University Leuven), Ghent, Belgium

    Hilde Breesch

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Ramon, D., Allacker, K., van Lipzig, N.P.M., De Troyer, F., Wouters, H. (2019). Future Weather Data for Dynamic Building Energy Simulations: Overview of Available Data and Presentation of Newly Derived Data for Belgium. In: Motoasca, E., Agarwal, A., Breesch, H. (eds) Energy Sustainability in Built and Urban Environments. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-13-3284-5_6

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