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Design optimization of renewable energy systems for NZEBs based on deep residual learning

Author

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  • Ferrara, Maria
  • Della Santa, Francesco
  • Bilardo, Matteo
  • De Gregorio, Alessandro
  • Mastropietro, Antonio
  • Fugacci, Ulderico
  • Vaccarino, Francesco
  • Fabrizio, Enrico

Abstract

The design of renewable energy systems for Nearly Zero Energy Buildings (NZEB) is a complex optimization problem. In recent years, simulation-based optimization has demonstrated to be able to support the search for optimal design, but improvements to the method that are able to reduce the high computation time are needed. This study presents a new approach based on deep residual learning to make the search for optimal design solutions more efficient. It is applied to the problem of system design optimization for an Italian multi-family building case-study equipped with a solar cooling system. Given a design space defined by set of variables related to Heating, Ventilation and Air Conditioning systems (HVAC) and renewable systems, a machine learning method based on residual neural networks to predict and minimize the objective function characterizing non-renewable primary energy consumptions is proposed. Results have shown that the approach is able to successfully identify optimized design solutions (energy performance improved by 47%) with good prediction accuracy (error smaller than 3%) while reducing the overall computation time and maximizing the exploration of the design space, paving the way for further advancements in simulation-based optimization for NZEB design.

Suggested Citation

  • Ferrara, Maria & Della Santa, Francesco & Bilardo, Matteo & De Gregorio, Alessandro & Mastropietro, Antonio & Fugacci, Ulderico & Vaccarino, Francesco & Fabrizio, Enrico, 2021. "Design optimization of renewable energy systems for NZEBs based on deep residual learning," Renewable Energy, Elsevier, vol. 176(C), pages 590-605.
    • Handle: RePEc:eee:renene:v:176:y:2021:i:c:p:590-605
    DOI: 10.1016/j.renene.2021.05.044
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    3. Tao Lv & Yuehong Lu & Yijie Zhou & Xuemei Liu & Changlong Wang & Yang Zhang & Zhijia Huang & Yanhong Sun, 2022. "Optimal Control of Energy Systems in Net-Zero Energy Buildings Considering Dynamic Costs: A Case Study of Zero Carbon Building in Hong Kong," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    4. Iijima, Fuyumi & Ikeda, Shintaro & Nagai, Tatsuo, 2022. "Automated computational design method for energy systems in buildings using capacity and operation optimization," Applied Energy, Elsevier, vol. 306(PA).
    5. Vassiliades, C. & Savvides, A. & Buonomano, A., 2022. "Building integration of active solar energy systems for façades renovation in the urban fabric: Effects on the thermal comfort in outdoor public spaces in Naples and Thessaloniki," Renewable Energy, Elsevier, vol. 190(C), pages 30-47.
    6. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    7. Ang, Yu Qian & Polly, Allison & Kulkarni, Aparna & Chambi, Gloria Bahl & Hernandez, Matthew & Haji, Maha N., 2022. "Multi-objective optimization of hybrid renewable energy systems with urban building energy modeling for a prototypical coastal community," Renewable Energy, Elsevier, vol. 201(P1), pages 72-84.
    8. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo & Russo, Giuseppe, 2022. "Energy virtual networks based on electric vehicles for sustainable buildings: System modelling for comparative energy and economic analyses," Energy, Elsevier, vol. 242(C).
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    10. Abokersh, Mohamed Hany & Gangwar, Sachin & Spiekman, Marleen & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2021. "Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept," Renewable Energy, Elsevier, vol. 180(C), pages 893-913.

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