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Predictive capability testing and sensitivity analysis of a model for building energy efficiency

  • G. KalogerasEmail author
  • S. Rastegarpour
  • C. Koulamas
  • A. P. Kalogeras
  • J. Casillas
  • L. Ferrarini
Research Article
  • 5 Downloads

Abstract

Building energy modelling presents a good tool for estimating building energy consumption. Different modelling approaches exist in literature comprising white-box/physical/calculation-based models, black-box/statistical/measurement-based models or hybrid models combining the former two. Our work presented in this paper deals with a calculation-based quasi-steady-state model for building energy consumption based on the ISO 13790 standard and its implementation in MATLAB/Octave. The model is also well compared to the ISO 52016 standard updating ISO 13790. The model predictive capability is confirmed against both EnergyPlus dynamic simulator results and calculation results of a commercially available relevant tool used as benchmarks. Machine learning techniques are applied to a large dataset of simulated data and a sensitivity analysis is presented narrowing down to the most influential model parameters.

Keywords

building energy modelling energy consumption assessment model sensitivity analysis model predictive capability 

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Notes

Acknowledgements

The work presented in this paper has been funded in the framework of “Support Tool for Energy Efficiency Programmes in medical centres — STEER” project, Grant Agreement 655694, Research and Innovation Staff Exchange (RISE), H2020 — MSCA — RISE — 2014, European Commission.

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Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • G. Kalogeras
    • 1
    Email author
  • S. Rastegarpour
    • 2
  • C. Koulamas
    • 1
  • A. P. Kalogeras
    • 1
  • J. Casillas
    • 3
  • L. Ferrarini
    • 2
  1. 1.Industrial Systems InstituteATHENA Research and Innovation CenterPlatani-PatrasGreece
  2. 2.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of Granada, CITIC-UGRGranadaSpain

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