From simulation to data-driven approach: A framework of integrating urban morphology to low-energy urban design

Wang, Wei; Liu, Ke; Zhang, Muxing; Shen, Yuchi; Jing, Rui; Xu, Xiaodong

doi:10.1016/j.renene.2021.08.024

Cited by 51 publications

(8 citation statements)

References 33 publications

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“…To address this, scholars have turned to data-driven approaches. Existing studies can be broadly grouped into two categories, the first employs data-driven tools for building energy consumption and, built environment assessment, aiming to expedite the design process of sustainable urban neighborhoods (Huang et al, 2022;Nutkiewicz et al, 2018;W. Wang et al, 2021); the second utilizes datadriven methods to identify building energy consumption across expansive urban neighborhoods, offering insights for energy retrofitting (Ali et al, 2020a(Ali et al, , 2020bYe et al, 2021).…”

Section: Data-driven Building Energy Predictionmentioning

confidence: 99%

“…On the one hand, the top-down approach, which depends on monitoring and statistics Wu et al, 2022), is often lacking in smaller cities or cities with insufficient economic development. On the other hand, the resources and time required for assessments that rely on bottomup approaches with energy consumption simulation engines often prove prohibitive in the early stages of planning (W. Wang et al, 2021). While scholars have recently turned to artificial intelligence and machine learning to predict building energy consumption, most studies focus on predicting the dynamic loads of building monoliths (L. Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Jiang,

Huang,

et al. 2024

FURP

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Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

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Section: Data-driven Building Energy Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Jiang,

Huang,

et al. 2024

FURP

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“…The evolutionary algorithm is the genetic algorithm that uses the natural selection principles to evolve a set of solutions towards an optimum solution (Machairas et al, 2014). Wallacei X ® is the key built-in and integrated multi-objective optimization algorithm widely employed in many studies (Wang et al, 2021). This tool tests each numeric value for each variable, test the results with objective functions, compares the results and goes to another set of variables which generate another solution (form).…”

Section: Optimizationmentioning

confidence: 99%

Integrating energy in the conceptual design stage to optimize building form

Vafaee¹,

Sandani²,

Khameneh³

et al. 2022

Linköping Electronic Conference Proceedings

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In architecture, Integrated Energy Design (IED) entails considering energy during each design phase, especially in the early design stage. The form of a building is an important factor in this stage due to its considerable impact on energy consumption. Finding the optimal form is a time-consuming process, and computational design techniques can help designers to facilitate this process and achieve a design solution with acceptable performance in terms of CO2 emission. Moreover, the surrounding buildings, trees and urban elements can affect the energy and daylight of the project by casting shadows. Considering all these elements throughout the design process can be very demanding and take several working days. Today, digital tools make it possible to parametrically analyze morphological characteristics of buildings to identify the most efficient solution. The present study proposes an environmental-simulation based design workflow to be used in the early design stage to determine the building’s form parameters (height, angle,..) in a given urban area based on the weather data and the surrounding context. This process is done by parametric design tools and environmental simulations in Rhino3D®, Grasshopper®, and ladybug Tools®. The typical Norwegian cabin’s form parameters are applied in the visual coding program (Grasshopper®) to generate the initial geometry for optimization. Due to the great effect of the energy consumption on the CO2 emission, minimizing energy, maximizing thermal comfort and the sky view percentage were the main objectives. To test the workflow the weather data of Tromsø (Norway) and 3d model of the surrounding context of a design location was applied as inputs. The output of this application was several building’s form alternatives for that specific location. This study showed using the digital tools and parametric design thinking can help the designers to apply the climatic data in the design process to narrow down the design solutions.

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“…e most important and primary role of cities is to serve as gathering places for people living in them. Human activities connect people with urban space [1][2][3][4]. After the rapid development of urban construction, many urban plots have changed towards simpli cation and homogenization, the urban vitality has been weakened, and various problems have emerged in urban development.…”

Section: Introductionmentioning

confidence: 99%

Urban Planning and Design Layout Generation Based on Artificial Intelligence

Wan

2022

Mathematical Problems in Engineering

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Today’s cities are becoming more and more complex, the spatial layout is gradually becoming more and more complex, and all aspects of urban construction that need to be considered are increasing. The traditional urban planning and design methods have encountered new challenges. Based on the unique perspective of urban “mesoscale,” this study attempts to apply artificial intelligence technology in the early stage of urban planning and design, predict the positioning of design land based on the surrounding environment, so as to break the limitations of manual decision-making, explore the spatial layout problem from the perspective of machine, find the correlation between land data, and generate results with certain reference value to assist decision-making. By delimiting the research area and collecting and processing data, we trained and generated the artificial neural network model and selected three different areas for model test. The test results verify the feasibility and effectiveness of the method process.

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From simulation to data-driven approach: A framework of integrating urban morphology to low-energy urban design

Cited by 51 publications

References 33 publications

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Integrating energy in the conceptual design stage to optimize building form

Urban Planning and Design Layout Generation Based on Artificial Intelligence

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