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Supporting the Smart Readiness Indicator—A Methodology to Integrate A Quantitative Assessment of the Load Shifting Potential of Smart Buildings

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  • Thomas Märzinger

    (Department of Material Sciences and Process Engineering, Institute for Chemical and Energy Engineering, University of Natural Resources and Life Sciences, 1190 Vienna, Austria)

  • Doris Österreicher

    (Department of Landscape, Spatial and Infrastructure Sciences, Institute of Spatial Planning, Environmental Planning and Land Rearrangement, University of Natural Resources and Life Sciences, 1190 Vienna, Austria)

Abstract

With the third revision of the Energy Performance of Buildings Directive (EPBD) issued in July 2018, the assessment of buildings now has to include a Smart Readiness Indicator (SRI) to consider the fact that buildings must play an active role within the context of an intelligent energy system. In order to support the development of the SRI, this article describes a methodology for a simplified quantitative assessment of the load shifting potential of buildings. The aim of the methodology is to provide a numerical, model-based approach, which allows buildings to be categorized based on their energy storage capacity, load shifting potential and their subsequent interaction with the grid. A key aspect is the applicability within the Energy Performance Certificate (EPC) in order to provide an easy to use calculation, which is applied in addition to the already established energy efficiency, building services and renewable energy assessments. The developed methodology is being applied to theoretical use cases to validate the approach. The results show that a simplified model can provide an adequate framework for a quantitative assessment for the Smart Readiness Indicator.

Suggested Citation

  • Thomas Märzinger & Doris Österreicher, 2019. "Supporting the Smart Readiness Indicator—A Methodology to Integrate A Quantitative Assessment of the Load Shifting Potential of Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1955-:d:233235
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    References listed on IDEAS

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    Cited by:

    1. Angela Popa & Alfonso P. Ramallo González & Gaurav Jaglan & Anna Fensel, 2022. "A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings," Energies, MDPI, vol. 15(9), pages 1-22, April.

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