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Building Simulation 2020, 13(1): 33-50 https://doi.org/10.1007/s12273-019-0559-8
Research Article | Issue | Published: 13 August 2019

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

Show Author's Information G. Kalogeras1( ),  S. Rastegarpour2,  C. Koulamas1,  A.P. Kalogeras1,  J. Casillas3,  L. Ferrarini2
Industrial Systems Institute, ATHENA Research and Innovation Center, Platani-Patras, 26504 Greece
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
Department of Computer Science and Artificial Intelligence, University of Granada, CITIC-UGR, E-18071, Granada, Spain
Keywords:
building energy modelling, energy consumption assessment, model sensitivity analysis, model predictive capability
Cite this article:
Kalogeras G, Rastegarpour S, Koulamas C, et al. Predictive capability testing and sensitivity analysis of a model for building energy efficiency. Building Simulation, 2020, 13(1): 33-50. https://doi.org/10.1007/s12273-019-0559-8
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Abstract Full text About this article

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.


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About this article

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

Show Author's information G. Kalogeras1( ), S. Rastegarpour2, C. Koulamas1, A.P. Kalogeras1, J. Casillas3, L. Ferrarini2
Industrial Systems Institute, ATHENA Research and Innovation Center, Platani-Patras, 26504 Greece
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
Department of Computer Science and Artificial Intelligence, University of Granada, CITIC-UGR, E-18071, Granada, Spain

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

References(27)

ASHRAE (2017). ASHRAE Standard 140. Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs (ANSI Approved). Atlanta, GA, USA: American Society of Heating, Refrigerating, and Air Conditioning Engineers.
Ballarini I, Corrado V (2012). Analysis of the building energy balance to investigate the effect of thermal insulation in summer conditions. Energy and Buildings, 52: 168-180.
DOI Google Scholar
Corrado V, Fabrizio E (2007). Assessment of building cooling energy need through a quasi-steady state model: Simplified correlation for gain-loss mismatch. Energy and Buildings, 39: 569-579.
DOI Google Scholar
Edilclima (2017). Edilclima EC 700. Available at https://www.edilclima.it/software-termotecnica/prog-termotecnica-energetica/scheda/700
EN 15265 (2007). Energy performance of buildings. Calculation of energy needs for space heating and cooling using dynamic methods. General criteria and validation procedures. BSI British Standards.
EnergyPlus (2015). U.S. Department of Energy’s (DOE). Building Technologies Office (BTO) and National Renewable Energy Laboratory (NREL).
DOI
Ferrarini L, Mantovani G (2013). Modeling and control of thermal energy of a large commercial building. In: Proceedings of IEEE International Workshop on Intelligent Energy Systems (IWIES), Vienna, Austria, pp. 149-154
DOI
Foucquier A, Robert S, Suard F, Stéphan L, Jay A (2013). State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews, 23: 272-288.
DOI Google Scholar
Garcia Sanchez D, Lacarrière B, Musy M, Bourges B (2014). Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods. Energy and Buildings, 68: 741-750.
DOI Google Scholar
ISO 13790 (2008). Energy performance of buildings—Calculation of energy use for space heating and cooling. International Standard. International Organization for Standardization.
ISO 13370 (2017). Thermal performance of buildings—Heat transfer via the ground—Calculation methods. International Organization for Standardization.
ISO 52016-1 (2017). Energy performance of buildings—Energy needs for heating and cooling, internal temperatures and sensible and latent heat loads. International Organization for Standardization.
Kalogeras G, Koulamas C, Kalogeras A, Moronis A (2018a). Precision robustness testing of a simulation model for energy use in buildings. In: Proceedings of the 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy, pp. 932-939.
DOI
Kalogeras G, Koulamas C, Kalogeras A, Moronis A (2018b). Verification and validation of a simulation model for energy use in buildings. In: Proceedings of the 14th IEEE International Workshop on Factory Communication Systems (WFCS), Imperia, Italy.
DOI
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017). Lightgbm: A highly efficient gradient boosting decision tree. In: Proceedings of the Neural Information Processing Systems Conference (NIPS), Long Beach, CA, USA, pp. 3146-3154.
Koulamas C, Kalogeras AP, Pacheco-Torres R, Casillasc J, Ferrarini L (2018). Suitability analysis of modeling and assessment approaches in energy efficiency in buildings. Energy and Buildings, 158: 1662-1682.
DOI Google Scholar
Le Norme UNI TS 11300 (2011). La normativa tecnica di riferimento sul risparmio energetico e la certificazione energetica degli edifici. (in Italian)
Google Scholar
Moronis A, Koulamas C, Kalogeras A (2017). Validation of a monthly quasi-steady-state simulation model for the energy use in buildings. In: Proceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus.
DOI
Progress ITCE (2017). Energy Technology Perspectives 2017 Excerpt Informing Energy Sector Transformations. Paris: International Environment Agency.
Quinlan JR (1992). Learning with continuous classes. In: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia.
Rastegarpour S, Ferrarmi L, Pacheco-Torres R, Kalogeras A, Koulamas C (2017). Sensitivity analysis of medical centers energy consumption with EnergyPlus. In: Proceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus.
DOI
Rastegarpour S, Ghaemi M, Ferrarini L (2018). A predictive control strategy for energy management in buildings with radiant floors and thermal storage. In: Proceedings of the SICE International Symposium on Control Systems (SICE ISCS), Tokyo, Japan.
DOI
Swan LG, Ugursal VI (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13: 1819-1835.
DOI Google Scholar
Tian W (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20: 411-419.
DOI Google Scholar
Van Dijk H, Spiekman M, De Wilde P (2005). A monthly method for calculating energy performance in the context of European building regulations. In: Proceedings of the 9th International IBPSA Building Simulation Conference, Montreal, Canada, pp. 255-262.
Wang S, Yan C, Xiao F (2012). Quantitative energy performance assessment methods for existing buildings. Energy and Buildings, 55: 873-888.
DOI Google Scholar
Wang Y, Witten IH (1997). Inducing model trees for continuous classes. In: Proceedings of the 9th European Conference on Machine Learning, Prague, Czech Republic.
Publication history
Copyright
Acknowledgements

Publication history

Received: 27 January 2019
Accepted: 16 May 2019
Published: 13 August 2019
Issue date: February 2020

Copyright

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

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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