Optimization of an office building form using a lattice incubate boxes method

Khalil, Amany; Tolba, Osama; Ezzeldin, Sherif

doi:10.1016/j.aei.2022.101847

Cited by 11 publications

(7 citation statements)

References 48 publications

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“…F(X) is the value of the objective function that shows the percentage of increase or reduction in annual thermal EUI, F(g) is the annual thermal EUI of a newly generated form, and F(b) is the annual thermal EUI of the base case. The reduction in EUI in comparison to the base case is shown with positive values, and the increase in EUI is shown with negative values [30].…”

Section: Optimization and Machine Learningmentioning

confidence: 91%

“…Settings in Octopus are population size = 100, elitism = 0.5, mutation probability = 0.2, mutation rate = 0.9, and crossover rate = 0.8. For each newly generated form, the percentage of increase or reduction in annual thermal EUI in comparison to the base case is calculated by using the following equation (Equation ( 1)) [30]:…”

Section: Optimization and Machine Learningmentioning

confidence: 99%

“…Lu et al [29] optimized two types of building forms (shoebox and irregular quadrilateral office buildings) for thermal EUI in Beijing, Shanghai, and Shenzhen. Khalil et al [30] optimized two types of cubic forms for thermal EUI, daylighting, and outdoor view percentage. Despite the fact that these studies considered more than one type of building form, the number and type of building forms were limited and almost all belonged to the same family.…”

Section: Introductionmentioning

confidence: 99%

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Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning

Khalil,

Lila,

Ashraf

2023

Computation

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The climate change crisis has resulted in the need to use sustainable methods in architectural design, including building form and orientation decisions that can save a significant amount of energy consumed by a building. Several previous studies have optimized building form and envelope for energy performance, but the isolated effect of varieties of possible architectural forms for a specific climate has not been fully investigated. This paper proposes four novel office building form generation methods (the polygon that varies between pentagon and decagon; the pixels that are complex cubic forms; the letters including H, L, U, T; cross and complex cubic forms; and the round family including circular and oval forms) and evaluates their annual thermal energy use intensity (EUI) for Cairo (hot climate). Results demonstrated the applicability of the proposed methods in enhancing the energy performance of the new forms in comparison to the base case. The results of the optimizations are compared together, and the four families are discussed in reference to their different architectural aspects and performance. Scatterplots are developed for the round family (highest performance) to test the impact of each dynamic parameter on EUI. The round family optimization process takes a noticeably high calculation time in comparison to other families. Therefore, an Artificial Neural Network (ANN) prediction model is developed for the round family after simulating 1726 iterations. Training of 1200 configurations is used to predict annual EUI for the remaining 526 iterations. The ANN predicted values are compared against the trained to determine the time saved and accuracy.

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Section: Optimization and Machine Learningmentioning

confidence: 91%

Section: Optimization and Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning

Khalil,

Lila,

Ashraf

2023

Computation

Self Cite

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“…Previous studies have also employed building optimization techniques to enhance energy performance. These approaches primarily focused on walls and their features as key variables in the optimization process, aiming to improve thermal comfort through factors such as thermal conductivity, as well as to enhance heat gain through parametric design strategies [3,35]. In countries with hot climates like Egypt, improving building efficiency is of paramount importance [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Thermal and Energy Performance of Clay Bricks with Recycled Cultivated Pleurotus florida Waste

Fahmy,

Ahmed,

Ali

et al. 2024

Buildings

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The development of energy-efficient and sustainable building materials is imperative to reduce energy consumption in the construction sector. This study addresses both the applied problem of increased solar heat gain and decreased indoor thermal comfort, as well as the scientific problem of reducing the thermal conductivity of clay bricks. It investigates the incorporation of recycled spent mushroom materials, consisting of Pleurotus florida mycelia and rice husk waste, as a novel additive in the production of fired clay bricks (FCBs) to enhance thermal insulation properties. The developed bricks were utilized in an optimized wall design for a residential building in New Cairo, Egypt. The wall design is created using energy modeling software, including Honeybee, Ladybug, Climate Studio, and Galapagos. The results demonstrate that an optimal waste content of 15% and a firing temperature of 900 °C yield the best thermal performance. Compared to traditional FCB walls, the new design incorporating the florida waste additive significantly improves thermal comfort, as indicated by a lower predicted mean vote and predicted percentage of dissatisfaction. Furthermore, the developed walls contribute to a reduction in CO2 emissions of 6% and a decrease in total energy consumption of 38.8%. The incorporation of recycled florida waste offers a sustainable approach to enhancing standard brick fabrication processes. This work highlights the promise of agricultural waste valuation for the development of eco-friendly and energy-efficient building materials. Future research should explore the mechanical strength, acoustics, cost–benefit analysis, and field implementation of the developed walls, thereby addressing both the scientific and applied aspects of the problem.

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“…Nevertheless, earlier research has only taken into consideration the view of the outdoors when changing the façade design, and it has not been integrated into the multi-objective optimization (MOO) of the building form for energy performance. Moreover, lighting design is frequently outsourced to outside experts late in the design process [21].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the View Percentage, Daylight Autonomy, Sunlight Exposure, and Energy Use: Data-Driven-Based Approach for Maximum Space Utilization in Residential Building Stock in Hot Climates

Kamel,

Khalil,

Lakousha

et al. 2024

Energies

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This paper introduces a comprehensive methodology for creating diverse layout generation configurations, aiming to address limitations in existing building optimization studies that rely on simplistic hypothetical buildings. This study’s objective was to achieve an optimal balance between minimizing the energy use intensity (EUI) in kWh/m2, maximizing the views percentages to the outdoor (VPO), achieving spatial daylight autonomy (sDA), and minimizing annual sunlight exposure (ASE). To ensure the accuracy and reliability of the simulation, the research included calibration and validation processes using the Ladybug and Honeybee plugins, integrated into the Grasshopper platform. These processes involved comparing the model’s performance against an existing real-world case. Through more than 1500 iterations, the study extracted three multi-regression equations that enabled the calculation of EUI in kWh/m2. These equations demonstrated the significant influence of the window-to-wall ratio (WWR) and space proportions (SP) on the EUI. By utilizing these multi-regression equations, we were able to fine-tune the design process, pinpoint the optimal configurations, and make informed decisions to minimize energy consumption and enhance the sustainability of residential buildings in hot arid climates. The findings indicated that 61% of the variability in energy consumption can be attributed to changes in the WWR, as highlighted in the first equation. Meanwhile, the second equation suggested that around 27% of the variability in energy consumption can be explained by alterations in space proportions, indicating a moderate correlation. Lastly, the third equation indicated that approximately 89% of the variability in energy consumption was associated with changes in the SP and WWR, pointing to a strong correlation between SP, WWR, and energy consumption. The proposed method is flexible to include new objectives and variables in future applications.

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Optimization of an office building form using a lattice incubate boxes method

Cited by 11 publications

References 48 publications

Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning

Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning

Enhancing the Thermal and Energy Performance of Clay Bricks with Recycled Cultivated Pleurotus florida Waste

Optimizing the View Percentage, Daylight Autonomy, Sunlight Exposure, and Energy Use: Data-Driven-Based Approach for Maximum Space Utilization in Residential Building Stock in Hot Climates

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