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An Integrated Energy Simulation Model for Buildings

Author

Listed:
  • Nikolaos Kampelis

    (Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, 73100 Chania, Greece)

  • Georgios I. Papayiannis

    (Mathematical Modeling and Applications Laboratory, Section of Mathematics, Hellenic Naval Academy, 18539 Piraeus, Greece
    Stochastic Modeling and Applications Laboratory, Department of Statistics, Athens University of Economics & Business, 10434 Athens, Greece)

  • Dionysia Kolokotsa

    (Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, 73100 Chania, Greece)

  • Georgios N. Galanis

    (Mathematical Modeling and Applications Laboratory, Section of Mathematics, Hellenic Naval Academy, 18539 Piraeus, Greece)

  • Daniela Isidori

    (Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy)

  • Cristina Cristalli

    (Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy)

  • Athanasios N. Yannacopoulos

    (Stochastic Modeling and Applications Laboratory, Department of Statistics, Athens University of Economics & Business, 10434 Athens, Greece)

Abstract

The operation of buildings is linked to approximately 36% of the global energy consumption, 40% of greenhouse gas emissions, and climate change. Assessing the energy consumption and efficiency of buildings is a complex task addressed by a variety of methods. Building energy modeling is among the dominant methodologies in evaluating the energy efficiency of buildings commonly applied for evaluating design and renovation energy efficiency measures. Although building energy modeling is a valuable tool, it is rarely the case that simulation results are assessed against the building’s actual energy performance. In this context, the simulation results of the HVAC energy consumption in the case of a smart industrial near-zero energy building are used to explore areas of uncertainty and deviation of the building energy model against measured data. Initial model results are improved based on a trial and error approach to minimize deviation based on key identified parameters. In addition, a novel approach based on functional shape modeling and Kalman filtering is developed and applied to further minimize systematic discrepancies. Results indicate a significant initial performance gap between the initial model and the actual energy consumption. The efficiency and the effectiveness of the developed integrated model is highlighted.

Suggested Citation

  • Nikolaos Kampelis & Georgios I. Papayiannis & Dionysia Kolokotsa & Georgios N. Galanis & Daniela Isidori & Cristina Cristalli & Athanasios N. Yannacopoulos, 2020. "An Integrated Energy Simulation Model for Buildings," Energies, MDPI, vol. 13(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1170-:d:328300
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    References listed on IDEAS

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    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Engel, J & Kneip, A, 1995. "Model Estimation in Nonlinear Regression," Papers 9510, Catholique de Louvain - Institut de statistique.
    3. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    4. Daniel Gervini & Theo Gasser, 2004. "Self‐modelling warping functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 959-971, November.
    5. Provata, Elena & Kolokotsa, Dionysia & Papantoniou, Sotiris & Pietrini, Maila & Giovannelli, Antonio & Romiti, Gino, 2015. "Development of optimization algorithms for the Leaf Community microgrid," Renewable Energy, Elsevier, vol. 74(C), pages 782-795.
    6. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    7. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
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    Cited by:

    1. Francesco Causone & Rossano Scoccia & Martina Pelle & Paola Colombo & Mario Motta & Sibilla Ferroni, 2021. "Neighborhood Energy Modeling and Monitoring: A Case Study," Energies, MDPI, vol. 14(12), pages 1-19, June.
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    3. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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