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Current software barriers to advanced model-based control design for energy-efficient buildings

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  • Atam, Ercan

Abstract

Fast and easy advanced model-based control design for energy-efficient multi-zone buildings is crucial for optimal energy savings, and this strongly depends on the availability and capability of advanced simulation and control design software and tools. In this paper, first a state-of-the-art review of the commonly used major software and tools by the community is done with respect to the barriers they present to advanced model-based control design for energy-efficient buildings. Next, the relevant novel concept of Functional Moke-up Interface is reviewed and the associated advances up to date are summarized. Finally, a set desired control-oriented features for new generation tools are given towards better solutions for energy-efficient building control designs.

Suggested Citation

  • Atam, Ercan, 2017. "Current software barriers to advanced model-based control design for energy-efficient buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1031-1040.
    • Handle: RePEc:eee:rensus:v:73:y:2017:i:c:p:1031-1040
    DOI: 10.1016/j.rser.2017.02.015
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    Cited by:

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    2. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    3. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    4. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    5. García Nieto, Paulino José & García–Gonzalo, Esperanza & Paredes–Sánchez, Beatriz María & Paredes–Sánchez, José Pablo, 2023. "Modelling energy performance of residential dwellings by using the MARS technique, SVM-based approach, MLP neural network and M5 model tree," Applied Energy, Elsevier, vol. 341(C).
    6. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    7. Faustino Patiño-Cambeiro & Guillermo Bastos & Julia Armesto & Faustino Patiño-Barbeito, 2017. "Multidisciplinary Energy Assessment of Tertiary Buildings: Automated Geomatic Inspection, Building Information Modeling Reconstruction and Building Performance Simulation," Energies, MDPI, vol. 10(7), pages 1-17, July.
    8. Zhang, Sheng & Cheng, Yong & Fang, Zhaosong & Huan, Chao & Lin, Zhang, 2017. "Optimization of room air temperature in stratum-ventilated rooms for both thermal comfort and energy saving," Applied Energy, Elsevier, vol. 204(C), pages 420-431.

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