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Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence: Case of Europoint Complex in Rotterdam

Author

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  • Berk Ekici

    (Chair of Design Informatics, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands)

  • Okan F. S. F. Turkcan

    (Department of Architecture, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands)

  • Michela Turrin

    (Chair of Design Informatics, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands)

  • Ikbal Sevil Sariyildiz

    (Chair of Design Informatics, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands)

  • Mehmet Fatih Tasgetiren

    (Department of Industrial and Systems Engineering, 3301 Shelby Center, Auburn University, Auburn, AL 36849, USA)

Abstract

The increase in global population, which negatively affects energy consumption, CO 2 emissions, and arable land, necessitates designing sustainable habitation alternatives. Self-sufficient high-rise buildings, which integrate (electricity) generation and efficient usage of resources with dense habitation, can be a sustainable solution for future urbanisation. This paper focuses on transforming Europoint Towers in Rotterdam into self-sufficient buildings considering energy consumption and food production (lettuce crops) using artificial intelligence. Design parameters consist of the number of farming floors, shape, and the properties of the proposed façade skin that includes shading devices. Nine thousand samples are collected from various floor levels to predict self-sufficiency criteria using artificial neural networks (ANN). Optimisation problems with 117 decision variables are formulated using 45 ANN models that have very high prediction accuracies. 13 optimisation algorithms are used for an in-detail investigation of self-sufficiency at the building scale, and potential sufficiency at the neighbourhood scale. Results indicate that 100% and 43.7% self-sufficiencies could be reached for lettuce crops and electricity, respectively, for three buildings with 1800 residents. At the neighbourhood scale, lettuce production could be sufficient for 27,000 people with a decrease of self-sufficiency in terms of energy use of up to 11.6%. Consequently, this paper discusses the potentials and the improvements for self-sufficient high-rise buildings.

Suggested Citation

  • Berk Ekici & Okan F. S. F. Turkcan & Michela Turrin & Ikbal Sevil Sariyildiz & Mehmet Fatih Tasgetiren, 2022. "Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence: Case of Europoint Complex in Rotterdam," Energies, MDPI, vol. 15(2), pages 1-35, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:660-:d:726858
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    References listed on IDEAS

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    1. Graamans, Luuk & Baeza, Esteban & van den Dobbelsteen, Andy & Tsafaras, Ilias & Stanghellini, Cecilia, 2018. "Plant factories versus greenhouses: Comparison of resource use efficiency," Agricultural Systems, Elsevier, vol. 160(C), pages 31-43.
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    1. Surajet Khonjun & Rapeepan Pitakaso & Kanchana Sethanan & Natthapong Nanthasamroeng & Kiatisak Pranet & Chutchai Kaewta & Ponglert Sangkaphet, 2022. "Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    2. Hou, D. & Evins, R., 2024. "A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).

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