Evaluating the impact of urban morphology on rooftop solar radiation: A new city-scale approach based on Geneva GIS data

Boccalatte, Antonio; Thebault, Martin; Ménézo, Christophe; Ramousse, Julien; Fossa, Marco

doi:10.1016/j.enbuild.2022.111919

Cited by 36 publications

(13 citation statements)

References 23 publications

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“…Of particular note recently is the growing interest in urban distributed photovoltaic power generation. Scholars combine GIS with UBEM to evaluate the PV potential of buildings (Boccalatte et al, 2022;Montealegre et al, 2022), paving the way for sustainable urban development.…”

Section: Urban Building Energy Consumption Modelingmentioning

confidence: 99%

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: Urban Building Energy Consumption Modelingmentioning

confidence: 99%

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Jiang,

Huang,

et al. 2024

FURP

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“…The density metrics used include BD, Sd height , average neighbourhood building height (ANBH), average building perimeter (AP), and envelope-to-roof surface area ratio. Boccalatte et al (2022) proposed a general GIS-based methodology that explored the impact of building height, building distance, building footprint, total floor area, mean building volume (MBV), volume-to-facade ratio, FAR, and the number of neighbourhoods on the rooftop overall shading rate at a city-scale. The density metrics that were common to most of these studies were BD, SC, and PR.…”

Section: Density Metricsmentioning

confidence: 99%

A least squares regression-based approach in the investigation of the influence of density metrics of 14 distinct Toronto neighbourhoods on the roof and facade solar potential

Hasan,

Zheng,

Horvat

2023

Front. Built Environ.

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Introduction: This Toronto-based study explores how density metrics relate to the solar potential of rooftops and facades of buildings in neighbourhoods differentiated by their use classifications. In the context of Toronto’s 2040 Net Zero Strategy, this research contributes insight on identifying neighbourhood types in Toronto that are suitable for undergoing retrofits of active solar technologies.Methods: The methodological approach adopted in this investigation mainly entails the selection of representative neighbourhood archetypes in the city; compilation of density metrics representing the neighbourhood morphological form and conducting solar analysis and regression assessments using relevant computational tools. By identifying 14 distinct neighbourhood archetypes and examining 20 relevant density metrics, the variation of roof and façade solar potential has been evaluated through a least squares regression-based approach.Results: The findings indicate a negative correlation between certain density metrics, such as the standard deviation of height, plot density, nearest neighbour ratio, and complexity with the roof solar potential, thereby demonstrating that certain neighbourhoods such as those categorized as Employment or Institutional may be more suitable for active solar technologies retrofits. Additionally, there is no significant relationship between most density metrics and façade solar irradiance, apart from the open space ratio, which only affects it moderately. Façade solar potential is unique to building position and orientation and can vary non-uniformly across neighbourhood-use classifications based on the extent of overshadowing inherent to that configuration.Discussion: The study provides valuable insights for urban planning and neighbourhood design, specifically in terms of density metrics that need to be considered when opting for active solar technology retrofits of existing Toronto neighbourhoods. Additionally, the study’s methodological approach can be emulated as a framework for future research exploring neighbourhood archetypes in other cities and climatic conditions. The findings of this research also contribute to promoting sustainable energy transition in Toronto’s neighbourhoods.

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“…Interactions between urban morphology and solar energy have been increasingly acknowledged [1]. Solar cadastres have proliferated over the past decade in many cities [2].…”

Section: Introductionmentioning

confidence: 99%

“…However, they generally focus on roofs and still few consider facades [3]. Most of the current tools and approaches to model the solar potential on facades are generally limited to the level of specific buildings or a group of buildings, but few consider the scale of city [4].This is explained by a methodological and technical approach that is complex to implement, in particular due to two aspects: (1) the reliable modelling of the reflected component, which is significant on vertical surfaces but also complex to process in a reasonable calculation time [5]; (2) the increased level of detail and more accurate characterisation of the texture of the facades, which enables the identification of the parts that can be valorised by solar panels (excluding the glazed surfaces or the balconies for example).…”

Section: Introductionmentioning

confidence: 99%

Solar potential on facades at urban scale: an integrated approach combining solar and digital building modelling

Desthieux,

Gressin,

Raybaud

et al. 2023

J. Phys.: Conf. Ser.

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The paper presents an integrated approach to improve the solar radiation modelling on facades in large-scale built-up areas. The modelling of the built environment must first be improved in terms of level of detail. Thus, from aerial oblique images, a digital twin of the urban scene is created, allowing to process the facades as textured objects and to detect windows using an artificial intelligence image processing. Reflected radiation is significant on vertical surfaces, but complex to model on large areas. The developed model is based on a simplified radiosity approach, which reduces the volume of analysis, storage and thus the computation time, while producing reliable results. A demonstration of the integrated tool is presented for an urban area in Geneva (1 km2), including a solar energy balance assessment for one of the buildings using the results from the solar modelling. The perspective is to generalise the approach to a larger scale and to complete the solar cadastre of the roofs of the Greater Geneva area with the facades.

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Evaluating the impact of urban morphology on rooftop solar radiation: A new city-scale approach based on Geneva GIS data

Cited by 36 publications

References 23 publications

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

A least squares regression-based approach in the investigation of the influence of density metrics of 14 distinct Toronto neighbourhoods on the roof and facade solar potential

Solar potential on facades at urban scale: an integrated approach combining solar and digital building modelling

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