Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms

Yan, Hainan; Yan, Ke; Ji, Guohua

doi:10.1016/j.buildenv.2022.109081

Cited by 56 publications

(10 citation statements)

References 43 publications

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“…Figure 4b presents the energy consumption simulation results for curtain wall vent heights of 0.3 m, 0.6 m, 0.9 m, 1.2 m, and 1.5 m under a 1.2 m cavity. It is worth stating that the chosen parametric approach has been applied several times in the field of energy consumption research and has been effectively practiced in different areas such as city block [52][53][54], single buildings [55,56], and double-layer curtain wall [20,21].…”

Section: Project Overviewmentioning

confidence: 99%

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

Liu,

Wang,

Ding

et al. 2024

Sustainability

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In the context of global warming, the focus on applying and researching double–skin facade (DSF) systems to reduce energy consumption in buildings has significantly increased. However, researchers have not thoroughly examined the performance and applicability of DSFs in severe cold regions with high winter heating demands. This study aims to evaluate the potential application of DSFs in the harsh cold cities of Northwest China and investigate their role in enhancing energy efficiency in large public buildings. Through energy consumption simulation and a comprehensive evaluation using the TOPSIS entropy weight method, the effects of applying 20 DSF schemes in four cold cities in Xinjiang (Kashgar, Urumqi, Altay, and Turpan) were analyzed. The experimental results indicate that the average EUI energy–saving rates in Kashgar, Urumqi, Altay, and Turpan are 64.75%, 63.19%, 56.70%, and 49.41%, respectively. South–facing orientation is deemed optimal for DSF in Xinjiang cities, with the highest energy–saving rate reaching 15.19%. In Kashgar, the energy–saving benefits of west–facing DSF surpass those of north–facing DSF. Conversely, the order of orientation benefits for other cities is south, north, west, and east. An analysis of heating, cooling, and lighting energy consumption reveals that Box Windows exhibit superior heating energy efficiency, while Corridors are more effective for cooling. This characteristic is also evident in the optimal installation orientation of various types of curtain walls. Given the relatively higher demand for heating compared to cooling in urban areas, Box Windows yields significant benefits when facing south, west, or north; conversely, if there is a high demand for urban cooling, Corridors should be considered in these three directions. Multistorey DSF systems are suitable for east–facing buildings in Xinjiang cities. Selecting suitable DSF schemes based on specific conditions and requirements can reduce building energy consumption. The research findings offer theoretical guidance for designing and implementing DSF in diverse cities in cold regions.

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Section: Project Overviewmentioning

confidence: 99%

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

Liu,

Wang,

Ding

et al. 2024

Sustainability

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show abstract

“…Overall, BPO leverages advanced simulation tools and algorithms to optimize building performance, considering various factors such as energy consumption, indoor air quality, and budget constraints [45]. By utilizing this approach, designers can create more sustainable and efficient buildings that meet the diverse needs of occupants while minimizing their environmental impact [46][47][48].…”

Section: Multi-objective Evolutionary Algorithmsmentioning

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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“…High-performance machine learning model is the core of accurate real-time prediction of soil properties, which is in line with the future research trend of WebGIS system. Although the choice of model is very important for WebGIS system to predict soil properties online, many studies have found that the low performance and prediction accuracy of the model are essentially caused by the mismatch of hyperparameters of the model [30][31][32][33]. However, there is a key shortcoming in traditional machine learning to predict soil properties, which requires manual or empirical adjustment of hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

Song,

Pan,

Xiang

et al. 2024

Remote Sensing

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The monitoring and evaluation of soil ecological environment is very important to ensure the saline-alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent needs of high-performance prediction of soil properties using multi-dimensional data in WebGIS system. For the end, this study aims to develop a saline-alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction and data management. The system visually displays the statistical results of air quality, vegetation index and soil properties in the study area. It provides users with ecological risk assessment functions to analyze soil heavy metal pollution. Specially, the system introduces a tree structured Parzan estimator (TPE) optimized machine learning model to achieve accurate prediction of soil salinity. The TPE-RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE-GBDT model, which exhibited the strong ability for the nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline-alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline-alkali soil ecological protection.

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Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms

Cited by 56 publications

References 43 publications

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

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

A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

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