TY - JOUR
T1 - Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence
T2 - Case of Europoint Complex in Rotterdam
AU - Ekici, B.
AU - Türkcan, Okan
AU - Turrin, M.
AU - Sariyildiz, I.S.
AU - Tasgetiren, Mehmet Fatih
PY - 2022
Y1 - 2022
N2 - The increase in global population, which negatively affects energy consumption, CO2 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.
AB - The increase in global population, which negatively affects energy consumption, CO2 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.
KW - Artificial intelligence
KW - BIPV
KW - Building performance simulation
KW - Computational optimisation
KW - Energy consumption
KW - Machine learning
KW - Metropolis
KW - Self-sufficiency
KW - Vertical farming
UR - http://www.scopus.com/inward/record.url?scp=85122899126&partnerID=8YFLogxK
U2 - 10.3390/en15020660
DO - 10.3390/en15020660
M3 - Article
SN - 1996-1073
VL - 15
JO - Energies
JF - Energies
IS - 2
M1 - 660
ER -