Renewable and Sustainable Energy Reviews 113 (2019) 109255
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
A digital workflow to quantify regenerative urban design in the context of a
changing climate
T
Emanuele Nabonia,∗∗, Jonathan Natanianb,∗, Giambattista Brizzia, Pietro Floriod,
Ata Chokhachianb, Theodoros Galanose, Parag Rastogif
a
Institute of Architecture and Technology, The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation, 1425, Copenhagen, Denmark
Chair of Building Technology and Climate Responsive Design, Dept. of Architecture, Technical University of Munich, Arcisstraße 21 80333, Munich, Germany
d
Solar Energy and Building Physics Laboratory (LESO-PB), École Polytechnique Fédérale de Lausanne (EPFL), LE 0 02 (Bâtiment LE) Station 18, CH-1015, Lausanne, VD,
Switzerland
e
Austrian Institute of Technology, Australia
f
Arbnco Ltd., Glasgow, UK
b
A R TICL E INFO
A BSTR A CT
Keywords:
Regenerative design
Climate change
Outdoor microclimate
Energy use
Renewable energy
Daylight
Biophilia
Parametric design
The regenerative approach to design goes beyond limiting the environmental impact of the built environment
and towards the enrichment of the ecosystem, adaptation to climate change, and the improvement of human
health. This concept is being applied to buildings through new standards such as the Living Building Challenge,
yet examples of implementation of regenerative design at the urban scale are rare. While this is a promising
direction for sustainable design, in theory new metrics, design tools and workflows need to be developed to
translate regenerative design concepts into practice effectively. Among other factors, barriers to implementation
remain rooted in the shortcomings of existing urban simulation tools to evaluate a wide range of performance
metrics simultaneously. This paper thus proposes a prototype workflow to evaluate regenerative performance
using existing evaluation tools in a single digital workflow. A series of existing and customised plugins, most of
which are already in use and open source, were integrated into a multi-parametric workflow based on the
Grasshopper visual programming tool. The workflow was tested on Malaga as a case study and incorporated key
performance indicators related to outdoor human thermal comfort, biophilia, daylight performance, and energy
use and production, based on data exchange and synergies across the different tools. These indicators were
evaluated for present and future climate scenarios obtained from a weather generator. This paper demonstrates
the potential of this workflow to receive visual feedback on various aspects of regenerative urban design, thus
enabling designers to more effectively pursue an evidence-based urban design process.
1. Introduction
Cities are critical to modern life: approximately 55% of the world's
population currently lives in urban areas and on average cities contribute more than 70% of most countries' Gross Domestic Products
(GDP) [1]. The number of urban dwellers is projected to grow by 68%
by 2050 [2]. Consequently, the built environment will continue to be a
significant contributor to global greenhouse gas emissions, accounting
for 50–60% of total emissions [3], 71–76% of emissions from final
energy use [4] and consuming about 75% of global primary energy. At
the moment, most urban dwellers (about 2 billion people) live in cities
that have a high risk of mortality and economic losses associated with
natural disasters [5], and extreme events disproportionately affect the
∗
approximately 1 billion “disadvantaged and vulnerable populations”
[4]. Thus, while cities continue to grow in importance and size, improving living standards and prosperity [1], they remain inequitable,
vulnerable, and polluted. This combination of population growth, increasing energy consumption and pollution, is expected to result in
higher environmental stress on urban infrastructure [6,7].
These phenomena include the effect of both systematic changes in
climate, such as warmer summers on average, as well as increased
probability and severity of extreme events such as heat waves. For
example, warmer days and nights along with higher air pollution levels
can contribute to general discomfort, respiratory difficulties and illness,
heat exhaustion, and heat-related mortality due to lack of night ventilation and trapped heat in the street canyons. Therefore, given the high
Corresponding author.
Corresponding author.
E-mail address: jonathan.natanian@tum.de (J. Natanian).
∗∗
https://doi.org/10.1016/j.rser.2019.109255
Received 10 February 2019; Received in revised form 28 June 2019; Accepted 28 June 2019
1364-0321/ © 2019 Elsevier Ltd. All rights reserved.
Renewable and Sustainable Energy Reviews 113 (2019) 109255
E. Naboni, et al.
proportion of total energy consumed, high global urbanisation rates,
and the vulnerability of cities to climate change, the urban built environment is coming to be seen as both part of the problem as well as
the solution. This indicates that the responsive design of cities needs to
go beyond climate change mitigation, i.e., reducing greenhouse gas
emissions by reducing energy intensity, to adaptation, by which cities
will be transformed into catalysts of both health and wellbeing. This
regenerative approach will enhance the creation of a thriving ecosystem system by focusing on the improvement of the interdependent
relationships between humans and the built and natural environments.
In terms of the regenerative built environment, this approach will
require a shift away from the narrow focus on building energy performance, mitigation strategies, and minimisation of environmental impacts to a broader framework that enriches places and their inhabitants,
ecology, and culture, and makes cities resilient to climate change and
changing human needs. Although many professionals declare sustainability as one of their primary drivers, finding examples, where regenerative design has been implemented at the urban level, is difficult.
Emerging codes and standards that incorporate a regenerative approach
to building design include the Living Building Challenge, WELL
building standard and One Planet Living. However, similar metrics do
not exist for urban design, and rules defined for buildings cannot always
be successfully scaled up to the urban scale, leading to a lack of Key
Performance Indicators (KPIs) for urban regenerative design. This article thus responds to these concerns in a workflow incorporating KPIs
based on the three pillars of regenerative design in practice proposed in
Ref. [8]: Climate and Energy, Ecology and Carbon, and Human Wellbeing and Health.
This paper begins by describing the overall concept of urban regenerative design. This is followed by a description of the areas of regenerative design and definition of KPIs that help address the challenges associated with its implementation. These metrics are computed
for both historical and predicted future typical years. As the tools
currently available for these evaluations do not cover all the KPIs defined here, a new and multi-layered parametric workflow is offered
based on data exchange and synergies across the tools. The paper thus
tests the potential of combining multi-domain objectives in design, focusing on the climatic and urban context of Malaga, Spain. The final
sections discuss the capabilities, application potentials, as well as the
limitations of the proposed workflow.
and building codes, including energy codes. Whereas zoning and energy
codes address several concerns related to health and sustainability, they
have been used to separate the design of cities into two disconnected
scales: a single building and its urban surroundings at different scales.
At the city scale, insufficient attention has been devoted to the creation
of pleasant microclimates and of the integration of vegetation and
nature. At the building scale, the influence of urban microclimate on
the indoor conditions has been ignored. Furthermore, a focus on energy
optimisation has diverted attention away from the quality of the indoor
environment, usually addressed with simplistic rules of thumb. This
section presents a review of existing knowledge about the extents to
which these approaches have long-lasting consequences for human
health, sustainability, and climatic resilience [9].
1.2.1. Climate change and urban heat islands
The relationship between humans, buildings, urban microclimates
and the global climate is complex. Urbanisation, particularly the densification of built spaces and populations, has given rise to the phenomenon of Urban Heat Islands (UHI). Furthermore, changes in landuse due to urbanisation are one of the two primary sources of anthropogenic effects on climate, the other being greenhouse gas emissions.
For example, a study of surface temperatures over the continental
United States found that half of the observed decrease in diurnal temperature range is due to urban and other land-use changes”, and that
0.27 °C of “mean surface warming per century [was] due to land-use
changes [9]. Further, warmer summer temperatures due to climate
change may exacerbate the effects of UHI.
State of the art in building energy performance is to use a typical
sample of weather based on historical records from the nearest weather
station, which is often peri-urban or rural. Several recent studies have
demonstrated the unsuitability of using only historical records for
evaluating long-term design choices [10]. UHI may worsen indoor
conditions or cause a further increase in energy demand, particularly
for cooling and ventilation [12]. For example, Mauree et al. [10] estimated that a university campus on the shores of Lake Geneva could see
an increase in cooling demand of 30% and 52% in 2069 and 2099,
respectively. Oke identified three main causes of microclimatic change
in cities [11]: (1) interception of short- and long-wave radiation by
buildings, (2) reduced long-wave heat emissions due to reduced sky
visibility, and (3) increased storage of sensible heat in buildings. A
dense building stock also lowers mean wind speed, resulting in even
higher mean air temperatures. Several strategies can be adopted to
mitigate and adapt to UHI in a changing climate, promoting outdoor
social life and potentially reducing building energy demand. For this,
accounting for both climatic changes and local thermal characteristics
is crucial in policies that address urban morphology, choice of materials
and human activity.
1.1. Objectives
This study aims to provide a strategy and tools for implementation
of urban regenerative design in practice. This is broken down into five
objectives: The first objective is to elaborate an urban regenerative
design framework that addresses the priority areas of climate and energy, ecology and carbon, and human wellbeing identified above; the
second is to establish, within these pillars, specific, quantifiable performative criteria for designers. These criteria are: building energy efficiency and solar electricity generation potential from Building
Integrated Photovoltaics (BIPV) (energy and renewables), indoor access
to daylight (daylighting), nature-connectedness (biophilia), and the
impact of urban microclimatic conditions on outdoor comfort (microclimate and outdoor comfort). The third objective is to establish a set of
KPIs that are understood by practitioners and reflect the shift from
conventional metrics which are unfit to quantify regenerative design;
the fourth objective is to create a digital workflow to calculate these
KPIs, using scripts and tools that are increasingly used in practice, and
may be customised to interlink various domains. The final objective is
to apply the workflow to a real case study in Malaga to gain insights on
limitations and opportunities for further developments.
1.2.2. Energy demand and renewable energy
While certifications and standards for buildings, e.g., the European
Union's Nearly Zero Energy Buildings (nZEBs) standards [12], promote
renewables, the optimisation of energy demand is usually conducted in
isolation without consideration of the urban environment or interactions with the surroundings. Yet, BIPV could potentially cover between
15% and 60% of the total electricity demand [13], and more than half
of the global PV capacity needed to reach the 2050 goals may be installed on buildings [14]. These trends are encouraged by a continuous
reduction in the price of solar technologies [15], increased efficiencies
of new technologies [15], and the many environmental and economic
benefits of distributed energy generation networks in comparison to
centralised networks [16].
However, the pursuit of maximum solar power generation may
conflict with other design intentions such as density or compactness due
to shading or lower amount of exposed façade surfaces. Façademounted photovoltaics may also result in smaller window areas, which
in turn may affect daylight levels and visual comfort. Thus, the trade-
1.2. Background
The practice of urban and building design is shaped by city zoning
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overall positive attitude towards the environment [27,28]. Along with
advantages such as flood protection, noise reduction, visual impact
attenuation and biodiversity [29], trees improve air quality [30,31] and
outdoor hygro-thermal comfort [32]. Urban green spaces may also
positively influence psychological wellbeing through proximity, encouraging exercise, and direct visual contact [33]. People with access to
green spaces feel more relaxed and positive [34,35], and have a lower
risk of cardiovascular disease [36]. The density and height of the urban
fabric may block access to the sky vault, another connection with
nature that is essential for human chronobiologic balance [37]. Space
openness, in combination with natural elements, may reduce anxiety
and impulsivity [38], while ensuring mitigation of the UHI effect
[39,40].
Subjective assessment techniques exist to infer these feelings from
surveys [41,42]. However, it is more useful for the urban design process
to be able to estimate nature-connectedness from information on the
distribution and attributes of vegetation, which stands for the main
natural feature available in cities. Indicators have been proposed to
correlate human health and wellbeing with urban greenery. For example, the Normalized Difference Vegetation Index (NDVI), the amount
of near-infrared radiation reflected from green areas into airborne detectors, is correlated with enhanced wellbeing at the broad territorial
scale [43]. Other useful indicators adapted to the regional scale are
proximity to vegetation [44,45] and visual contact [46], but these are
seldom considered in combination with wellbeing. Finally, the sky view
factor, which refers to the sky seen by surfaces, is relevant for the assessment of UHI, whereas the sky exposure factor, refers to the sky seen
by a point in space, is more significant in terms of psychological impact
on personal wellbeing [47].
offs between energy demand and supply should be another critical reference in an environmental urban design workflow. This trade-off is
usually expressed as the fraction of energy demand covered by renewables, coupled with the system payback period or the Levelized
Cost of Energy (LCOE) throughout the building lifecycle.
1.2.3. Outdoor comfort
Any approach to urban design must address the reality of an indoordominant modern lifestyle and proactively work to make outdoor
public spaces as comfortable and accessible as possible. To improve the
outdoor urban thermal conditions, pedestrian thermal comfort must be
calculated, precisely and methodically. However, outdoor comfort is
often neglected in the conventional practice of urban design [17] since,
compared to indoor spaces, outdoor comfort is quite difficult to estimate. This is primarily due to the higher spatial and temporal variability of important microclimatic variables such as short- and long-wave
thermal radiation, wind speed and direction, air temperature and humidity [18], and the complexity of the physical, physiological, or psychological processes triggered to adapt to these outdoor conditions
[19,20].
To account for outdoor comfort, the most complete used metric is
the Universal Thermal Climate Index (UTCI) [21], which provides a
bio-meteorological assessment of the outdoor thermal environment
based on the dynamic physiological response predicted by a model of
human thermoregulation, coupled with an adaptive clothing model.
The UTCI “equivalent temperature for a given combination of wind
speed, radiation, humidity and air temperature is … the air temperature
of the reference environment, which … produces the same response
index value”, i.e., “… an equivalent dynamic physiological response.”.
A UTCI assessment scale relates the UTCI range to different stress categories. In this paper, an application of UTCI in the regenerative urban
design workflow is shown.
1.2.6. A framework for digital integration
Urban modelling has often concentrated on isolated sustainable
performance metrics [48]. Renewed interest in the impact assessment
of urban form, building types, and overall design on energy demand
and supply has generated a variety of methods supported by different
tools in a range of resolution levels and capabilities [49,50]. This shift
to the district and city scales is also reflected in the evolution of energy
modelling tools – from Building Energy Modelling (BEM) to Urban
Building Energy Modelling (UBEM) such as CitySim, UMI, CEA, and
Ladybug Tools [51]. CitySim [52] can quantify the energy demand at
the urban scale as well as the outdoor radiative environment, with high
spatial resolution. The Urban Modelling Interface (UMI) [48], a Rhinobased urban modelling design tool, calculates operational energy use,
embodied energy, daylighting and walkability at the urban scale. The
City Energy Analyst (CEA) [53] was developed to evaluate urban energy use and energy system rationalisation. It was created to analyse
different urban scenarios using energy, carbon emissions, and financial
criteria, for which a Grasshopper interface is currently under development.
The coupling of simulation engines for detailed and multi variable
urban environmental performance analysis has been explored by a few
studies; Mauree et al. [10] coupled a meteorological model (the Canopy
Interface Model) with CitySim, and some studies have linked the Urban
Weather Generator (UWG) [54] with energy simulation tools to account
for the impact of urban microclimates on energy cooling demand and
energy usage intensity [55,56]. Other examples of integration at the
urban scale are the works of Natanian and Auer [57], who coupled
Radiance and EnergyPlus in a Grasshopper parametric workflow to
evaluate daylight potential, energy demand, and solar energy potential
in numerous urban block scenarios. Nault et al. [58] coupled EnergyPlus and Radiance/Daysim to develop a predictive model for urban
solar potential. Finally, Perini et al. [59] studied outdoor thermal
comfort and energy by coupling Envi_Met with TRNSYS to account for
radiant temperature in an urban canyon outdoor environment. Ladybug
tools in grasshopper potentially offer added benefits to this coupling,
which are yet to be fully explored. Mackey et al. [60,61] demonstrated
1.2.4. Daylight in urban residential buildings
Implementing design and evaluation tools in urban design is important since decisions made at this scale, such as massing proportions
and window to wall ratios impact the solar and daylight potential of the
individual buildings [22]. Designing to maximise daylight area and
access to natural light is one of the main ways of increasing human
health and wellbeing in buildings [23]. Daylight availability has served
as the main representative metric to evaluate daylight performance
through computational modelling.
In the case of residential buildings, one of the most commonly used
metrics is the daylight factor, although it has recently been superseded
by dynamic indices to represent annual conditions. Several dynamic
performance metrics for daylight exist to evaluate the design of nonresidential buildings, e.g. Continuous Daylight Autonomy (CDA), Useful
Daylighting Illuminance (UDI) or Spatial Daylight Autonomy (sDA).
While these metrics have been widely used, they have several
shortcomings: they do not distinguish between high and low illuminance ranges; they do not help evaluate direct sunlight except for detecting the potential for glare; and do not integrate the effects of seasonal daylight availability. In addition, multifunctional spaces in
residential buildings with overlapping and diverse activities and social
and cultural factors make it difficult to generate accurate schedules of
occupancy for daylight analysis [24]. Despite these limitations, this
work does not propose a new metric and uses the sDA metric, widely
used in previous studies, because it can be used to evaluate daylight
performance using one indicative number.
1.2.5. Wellbeing and contact with nature
Improving urban inhabitants’ access to, and connection with nature
is important for the “urban agenda” of this century [25]. The essential
elements of direct biophilic experience are light, air, water, plants,
animals, weather, natural landscapes, and fire [26]. An increased
connection with nature may improve concentration and create an
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a Ladybug-based workflow in which outdoor comfort was evaluated
using microclimatic and energy modelling with OpenFOAM and EnergyPlus, respectively. In this paper, this workflow has been extended
with several other algorithms that support the development of regenerative features. The next section introduces the regenerative design
workflow, describes its components and exemplifies its application on
an urban block in Malaga, Spain.
modelling UHI, based on neighbourhood-scale energy balances. Processing the ‘future’ weather time series created by the synthetic weather
generator through UWG is necessary to account for urban effects since
the synthetic generator used to develop future typical files, Indra
[62–64], works with weather data available from a weather station only
and does not account for spatial effects.
2.1.3. Outdoor microclimate
A suitable comfort model may be selected by addressing the following three aspects: (1) climatic conditions, (2) urban environment,
and (3) physical characteristics of the pedestrians. The UTCI metric was
selected here due to its capability to account for these three aspects, as
well as due to its integration in the Ladybug toolkit [65,66]. The calculation of UTCI relies strongly on the accuracy of the outdoor Mean
Radiant Temperature (MRT) calculation [60] since outdoor thermal
comfort is linked to the indoor thermal behaviour of buildings (Fig. 4).
The calculation first estimates longwave radiation based on surface
temperatures using the EnergyPlus simulation engine. During this step,
the hourly thermal demand of the buildings overlooking the given
street canyon is also estimated. View factors for every surface are calculated using the ray-tracing capabilities of Radiance [67].The temperature of each building surface viewed from the face of a target point
in the outdoor space is calculated as a weighted average temperature,
where the weight is defined by the area fraction of surrounding surfaces, visible from that given point. Calculations also consider the sky
temperature and the consequent radiative loss to the sky. The calculated longwave contribution is then augmented with the shortwave
solar radiation that falls on people using the SolarCal model, which is a
part of ASHRAE-55 thermal comfort standard [68]. In this respect, the
model provides both the hourly dynamic thermal load of the buildings
overlooking the canyon and the outdoor thermal performance measured with the Universal Temperature Climate Index (UTCI). In comparison to other existing approaches, the workflow proposed in this
paper offers advantages by allowing for the complete modelling of both
indoor and outdoor thermal fields in a continuous and contiguous
manner, and in Grasshopper, a robust parametric design optimisation
environment during early stages of urban design.
2. Methodology
Naboni and Lavinga [8] have discussed a series of methods and tools
to quantify regenerative design (Fig. 1). The idea is that regenerative
design needs to be quantifiable using clear metrics to measure the impact of strategies and solutions. This suggests the creation of reasonable
future climate estimates and KPIs for Urban Heat Island, outdoor
comfort, energy, daylighting and biophilia. In this paper, these KPIs are
translated and integrated into a digital workflow that responds to the
needs of researchers and practitioners that are accustomed to digital
processes supported by algorithmic and parametric design.
2.1. The urban regenerative design digital workflow
The proposed conceptual workflow brings together many disciplines
and spatial and temporal scales in a Grasshopper-based workflow. A
toolchain is developed by assembling separate scripts handling: (1) the
generation of future and urban weather files, (2) the calculation of
UTCI, (3) the calculation of building energy demand and supply, (4) the
simulation of indoor daylight, (5) the prediction of nature and sky view
for pedestrians. The digital workflow proposed in this paper is summarised in Fig. 2. It is designed to leverage the synergies and interdependencies between the different aspects of urban and building
performance evaluations. Fig. 3 names the specific plugins and explains
the set of algorithms that are used by the tools. For example, Honeybee's dynamic thermal calculation is used for the determination of
building energy demand as well as for simulating building surface
temperatures that affect the shortwave heat exchanges and, consequently, the UTCI. The following sections describe the workflow components in detail.
2.1.4. Building performance and renewables
The energy performance of the buildings was calculated using
EnergyPlus [69] via the Grasshopper plugin Honeybee. Following the
thermal zoning method, which was used in other district-scale energy
assessment workflows and guidelines [48,57], this study relied on an
automated division of each building to thermal zones. The first step is to
divide each building mass into floors according to predefined floor
height, and then to isolate each level into core and perimeter zones.
This division enables the consideration of the different impact of solar
gains on energy demand in different orientations. At this scale, it is
acceptable to assign, to both core and perimeter zones, the same input
parameters including occupancy patterns.
2.1.1. Future weather files
The future climate files used here are created using the synthetic
weather generation algorithm described in Refs. [62–64] and subsequently named Indra.1 The method creates samples of probable future
weather using a combination of historical data and climate change
model outputs (Global and Regional Climate Models). It maintains
psychrometric consistency and correlation between the significant
variables that affect urban and building comfort and energy use. In the
original publications, the authors suggest the use of multiple plausible
samples of future weather to obtain estimates of distributions of simulation outputs. However, due to computational constraints, this
workflow limits itself to representations of ‘typical’ future weather – an
issue to be rectified in future work.
2.1.5. Daylighting
The daylight modelling was carried out using Honeybee parametric
interface, based on Radiance and Daysim engines for raytracing [67].
The workflow quantifies the overall indoor daylight availability, accounting for urban effects while assuming uniform window-to-wall
ratios and using the same or similar divisions into core and perimeter
zones as in the energy modelling step.
2.1.2. Urban weather files
To account for the urban microclimate, the Dragonfly plugin2 for
the Urban Weather Generator (UWG) [54] in Rhinoceros 3D was used.
UWG approximates the changes in conditions inside urban canyons
compared to measurements at a weather station in an open area outside
a city. The tool can be used alone (as a data pre-processing step) or
coupled with existing programs (the co-simulation approach). UWG
calculates the hourly values of urban air temperature and humidity by
2.1.6. Biophilia, sky and nature view factor
The computation of the sky view factor is straightforward and
computationally fast with the view analysis component featured by
Ladybug. It is sufficient to provide the analysis surface (representing the
surface where pedestrians may be positioned) and the overall context
geometry, including building masses as input, to return the view or
exposure factor on a raster grid sampled on the analysis surface at a
1
The latest iteration of the source code is available at https://github.com/
paragrastogi/SyntheticWeather.
2
https://github.com/chriswmackey/Dragonfly.
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Fig. 1. The regenerative design workflow proposed in this paper.
Fig. 2. A schematic of the workflow and Key Performance Indicators (KPI).
do with its spotlight. The script proposed here performs an isovist
analysis through the dedicated Grasshopper native component: the
overall fraction of Lines Of Sight (LOS) intercepted by the footprints of
tree canopies throughout a complete round turn would give the planar
tree view factor. This fraction of tree-intercepted LOS can be computed
by filtering the indices of obstacles struck, as returned by the Grasshopper isovist component that matches the tree footprint polygons.
custom resolution. To account for the nature view factor, the method
accounts for the fraction of the visual field occupied by vegetation for
several pedestrian positions on the public space. This fraction is calculated from the projection of the tree canopies on a spherical visual
field in a 3D space [70], but quick and immediate feedback is given by a
planar section of the pedestrian visual field at eye height. An advantage
of this procedure is the absence of complex 3D manipulations of point
clouds representing trees.
A bi-dimensional isovist is the polygonal locus of all visible, unobstructed points from the generating viewpoint, namely the vantage
point, in a plan view [71]. Lines of sight are projected by pedestrian
observers in all directions on a horizontal plane as a lighthouse would
2.2. Simulation inputs and settings
The workflow was tested using the Barrio of La Luz (Fig. 5) in
Malaga, built around 1960. The regenerative performance of the site is
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Fig. 3. A schematic of the workflow used plugins and definition of inputs and outputs.
Fig. 4. The UTCI calculation workflow here diagrammed into more details.
The following descriptions focus on the inputs of the simulation
settings in each one of the evaluation modules. The computational
demands of both thermal and wind comfort calculations limited both
the number of simulated wind directions and the temporal coverage of
the final comfort assessment and climate change. The albedo values of
materials used in these are given in Appendix C.
Owing to computational constraints, the samples from the Indra
future climate generator were post-processed to create hypothetical
Typical Meteorological Year [59] files for the 2050s and 2080s. This is
an incomplete assessment from the perspective of quantifying uncertainty due to future weather, and future work will improve the integration of sampling to obtain distributions. The historical TMY is
not a matter of discussion in the paper, as the objective is to verify the
workflow's ability to represent regenerative performance and the
adaptability for practice. A brief description of the site can be found in
Appendix A. The geometric model that served as a base for the full set of
algorithms was created in Rhinoceros 6, a commercial NURBS 3D
software. The five blocks within the area of interest were modelled
explicitly, with automated Grasshopper parametric subdivision of floors
and window-to-wall ratio. The urban landscape was shaped to include
buildings up to 250 m away from the area of interest for Computational
Fluid Dynamics (CFD) and UHI modelling, though buildings outside the
area of interest were approximated as simple footprint extrusions
(Fig. 6).
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directions is generally low (< 1 °C), compared to using all available
wind information [60]. The surrounding urban context was explicitly
modelled only for a radius of 250 m around the area of interest, with the
impact of the urban masses outside that radius being approximated
using an appropriate roughness factor typical for urban areas, according
to the Updated Wieringa Roughness Classification [72]. The CFD study
presented here uses simplified urban models which consider massing
and not smaller architectural details which have minimal impact on
wind flow.
Elements such as awnings and facade details, were not considered.
Additionally, the effect of trees on wind movement was not included in
the simulations both because of time constraints and the insufficient
data available on the accurate location and 3D shape of trees. Finally,
the impact of air temperature and buoyancy on the wind flow was not
considered, and an isothermal environment was assumed for the whole
area. Typically, the effects of temperature differences on the wind flow
is minimal, especially when there are no large heat sources in the urban
environment. As mentioned earlier, quantifying thermal comfort requires the calculation of a variety of parameters at different steps. First,
the geometrical model of the area is developed. Then, the CFD cases are
generated and run. Wind factor values at each point of the mesh are
then calculated and extracted during run-time. Wind factors are calculated by dividing wind velocity values with the reference wind velocity at 10 m. The selection of height is not random but follows the
requirements of the UTCI polynomial expression requiring input velocities at 10 m.
A custom Grasshopper component, using meteorological data,
translates the wind factors for each point to yearly wind velocity
schedules, 8760 wind velocity values for each point (Fig. 7). The velocities which correspond to each analysis period are then used, along
with MRT values for all surfaces, to quantify UTCI values at each point
assessed. One advantage of this approach is that the output is a schedule
of values, for all hours within the analysis period determined, allowing
for various visualisations in the form of thermal comfort maps to be
produced. Additionally, allowing for temperatures to be included would
then mean that multiple, hourly wind simulations would be required to
assess wind flow as temperatures vary greatly both due to climatic
conditions and urban context. More CFD settings can be found in
Appendix D.
For the Building Energy Performance Simulation (Energy Use
Intensity and Energy Generation), the depth of the perimeter zone was
set to 6 m, corresponding to twice the height of the zone [48]. The
geometrical model considers a window to wall ratio of 40%. Inputs
parameters for the energy simulation were defined based on the
Spanish energy code [73] for climatic zone 3A (Appendix E). The radiation analysis drove the selection of the envelope surfaces for the
BIPV energy yield calculations. The following viability thresholds were
assumed for annual irradiance rates: 800 kWh/m2 for facades and
1000 kWh/m2 for rooftops. Floors subdivided the facade surfaces, and
these thresholds were tested against the average radiation levels for
each storey (Fig. 8). The selected surfaces were then used for the PV
Fig. 5. Perspective view of the site, La Luz in Malaga (google coordinates:
36.696504, -4.455054).
Fig. 6. The 3D image of the simplification process for performance evaluation.
labelled with the current year (2018) to indicate that, if climate change
is not considered in the design and simulation process, this is the best
estimate of weather data currently available. Specific settings can be
found in Appendix B.
Since the Indra algorithm uses the IPCC climate change projections,
an increase in average annual temperatures, along with increases in the
severity of extreme summer days [7], is expected for most locations on
earth. The magnitude of the difference varies with location and time
into the future. As the original publications point out, the algorithm
does not itself project data into the future. Rather, it scales the historical
distribution of weather parameters using the IPCC model outputs and
resamples them.
Wind simulations were done for the four prevailing trends in
Malaga, according to local meteorological data. Previous studies have
shown that the error in UTCI calculation using the four prevailing wind
Fig. 7. Wind map for the four main directions selected for the outdoor microclimate analysis.
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obstructions are constituted by the buildings and the tree canopies. The
fraction of lines of sight over 360° from a viewpoint intercepted by the
footprints of tree canopies would give the planar tree view factor. The
computationally intensive intersections of 360 LOS (1 per degree) can
be run independently per each viewpoint in a batch cycle that has been
implemented in the current workflow, through the animation of a
Grasshopper integer slider.
3. Simulation outputs
3.1. Urban weather file
To account for the impact of Urban Heat Island on the simulations,
Urban Weather Generator (UWG) [54] was used to modify the historical climate data obtained from Malaga airport and the future time
series generated by the synthetic weather generator Indra based on this
airport data. UWG approximates thermal conditions within an urban
area using several key geometric and material variables including
building height, coverage, facade-to-site ratio, and road, roof, wall, and
window constructions [60]. UWG uses a generic urban canyon model to
run an energy balance calculation and produce the urban-adjusted
weather time series (Fig. 10).
Fig. 8. Annual radiation analysis for potential photovoltaic studies. The roofs
have more potential than facades, as expected.
3.2. Outdoor thermal comfort
Thermal comfort assessment, on the other hand, was limited to the
extremely Hot, Extremely Cold, and Typical Spring weeks (Fig. 11),
with the aim to capture periods with both average and extreme conditions.
3.3. Building energy performance (Energy Use Intensity and Energy
Generation)
Figs. 12–15 show different energy demand, and energy balance
evaluations produced from this workflow for three different climatic
scenarios in different time frames (hourly and monthly). This analytical
variability can be used for different evaluation proposes and can indicate the impact of future climate on energy demand in different time
frames, as well as the impact of urban microclimate on the energy
balance. Furthermore, energy evaluation can be conducted in conjunction with other environmental metrics and indicate the trade-off
between them in various morphological and usage scenarios.
Fig. 9. The Energy and Daylighting model, including the surrounding buildings
that cast shadows or reflect light.
energy calculation using 17% efficiency and 70% area coverage for
each of the surfaces above the radiation thresholds. A DC to AC deratement factor of 0.85 was used, and the effect of trees was neglected.
The daylighting simulation was based on the same model used for
the energy simulation, which is an acceptable approximation at this
scale. Only the context that generates shadows or contributes to light
reflection is modelled (Fig. 9). The simulation settings regarding Radiance parameters and material definitions are summarised in
Appendix F. For the calculation of view factors, viewpoints are sampled
on the boundaries of sidewalks with a spacing of 5 m; the view
3.4. Daylighting
The sDA (300 Lux/50%) ranges vary from 40% to 80% on the outer
shell of the district, while this range drops to between 13% and 50% in
Fig. 10. Representation of the local yearly temperatures measured at Malaga Airport, a rural location [top left], and the modified urban weather time series, at the
urban project site [lower left] obtained from Urban Weather Generator (UWG). The chart at right zooms into a 24-h cycle.
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E. Naboni, et al.
Fig. 11. A schematic overview of the calculation of the Universal Thermal Climate Index (UTCI).
be considered as a low value, mainly associated with the minimal distance to a visible tree, which is, on average, 28 m (Figs. 18–19).
Moreover, compared to the WELL standard, the assessed area features
only 5% of vegetation, which corresponds to ⅕th of the requirement.
4. Discussion
4.1. Capabilities of the workflow
The workflow allows for the consideration of interconnected, multiscale factors in conjunction with multiple predictions of future weather
conditions to improve the estimation of urban resilience. Simulating
many weather conditions should not increase confidence in characterising the probability of different weather conditions. Instead, the procedure helps distinguish the response of the urban environment to
differing external climatic stressors, which in turn will help to build in
resilience into the design.
Furthermore, the ability to effectively and automatically generate
thermal zones out of the district's geometry through Grasshopper can
make outdoor comfort, energy demand and daylighting evaluation far
more integrated into the design process. Adding energy production
considerations can be achieved just as seamlessly, and lead to the critical energy balance evaluation between supply and demand. For biophilia, an easy-to-calculate metric quantifying the visual prominence of
vegetation in outdoor urban areas offers an objective and numerical
scale to determine whether the presence of trees and green areas is
Fig. 12. A visualisation of the Cooling energy demand of each building.
the inner core of the neighbourhood (Fig. 16). The simulations could be
carried out for different climate periods.
3.5. Sky and nature view factor
Fig. 17 shows that the smallest road sections in the study area
feature a low sky-view factor of less than 30% while the central square
has a factor higher than 80%: as a reference, Times Square in New York
features a sky-view factor equal to 37%. Trees are currently not considered in the analysis of sky view. In the proposed case-study, on
average 25% of the circular visual field is occupied by trees. This may
Fig. 13. The monthly breakdown of energy demand across all buildings by end-user type.
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Fig. 14. Hourly energy demand curves for a cold (left) and hot (right) days in the historical, 2050, and 2080 typical files. The energy requirements for the hot day are
larger for future files than for historical ones, while the energy requirement for a cold day decreases into the future.
sufficient in a pre-design phase. This indicator can be effectively used to
justify compliance with emerging building standards such as WELL.
Moreover, it may add a psychological wellbeing metric to the current
outdoor comfort indices.
4.2. Limitations
The incorporation of uncertainty into our model is rudimentary for
now because climate change integration is limited to the inclusion of
‘typical’ (median) future files. This has the potential to miss many
features of concern, such as heat waves and extreme urban heat stress.
While the synthetic weather generator can generate any number of
plausible future year samples, including years with extreme events, the
computational cost of urban calculations makes the use of many files
prohibitively expensive. The same limitation applies to the characterisation of UHI, where computational resources and time represent a
limit. This impacts the number of wind directions and the resolution
Fig. 15. Monthly energy demand and supply for the three different climatic
periods. Substantial energy use intensity level was recorded in summer, compared to negligible changes in PV production.
Fig. 16. Continuous daylight autonomy and spatial daylight autonomy representations.
Fig. 17. Sky View Factor. Sky view factor on a 1 m raster grid, computed in the plan (left) and axonometric (right) view.
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performance during the design process. However, the generation of
many design options that are informed by the corresponding performance measures creates a need for intelligent spatial design exploration
to sort, quantify and select the best scenarios.
4.4. Applicability in practice
Different tools in this prototype workflow are integrated by means
of custom scripts (programs) written in Grasshopper. The advantage
designers working with a visual tool is the ease of incorporating visualisations for KPIs, primarily through the creation of operational or
functional displays. This workflow highlights the spatial and temporal
aspects of the different KPIs. The prototype presented here shows that
the visualisations are informative and intuitive since they relate a KPI
directly back to geometry (Fig. 20). This could aid in the evaluation of
trade-offs between energy demand and solar production hour-by-hour
and seasonally, or between increasing vegetation and view of the sky or
indoor daylighting. The potential to create a sustainable environment
by integrative parametric urban planning is promising since this
workflow allows the further incorporation of additional analyses and
simulation.
Although the framework does not yield results with the click of a
button, the correlation or the simultaneous visualisation of multiple
analyses can substantially help in performance-driven decision making.
The integrative urban planning workflow still strongly relies on expertise in Grasshopper and environmental analysis, and reducing this
reliance on the user's expertise is an area of future work.
Fig. 18. Nature View Factor, construction. Lines of sight intercepted by the tree
canopies footprints (blue) vs those caught by buildings (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the
Web version of this article.)
and extent of the domain for CFD simulations.
Furthermore, simplifications in the UHI assessment, CFD simulations and energy simulations may affect the confidence in the outdoor
comfort calculation. Finally, the calculation of visual biophilia and
daylight indicators is limited by the computational intensity of raytracing of light paths and lines of sight. Including the geometry of trees
would improve these calculations as well. Computational limitations
may potentially be addressed by improving parallelisation and offloading the computation from a user's computer to cloud-based simulation services. In addition, some of the simulation and analytical
processes of this methodology were carried out separately, where a
process is started manually after the preceding one has finished. This,
along with data management challenges associated with coupling these
tools, needs to be addressed in future developments of this methodology.
5. Conclusion
To ameliorate the thermal quality of the urban environment, cities
should offer their residents healthy outdoor spaces, favourable microclimates, and buildings that operate with moderate energy costs and
nearly-zero carbon emissions. Urban spaces should enhance environmental conditions such as contact with nature, view of the sky, interior
daylighting and more. These goals should be achieved holistically and
inter-connectedly; pursuing regenerative design at the urban scale
cannot be fulfilled with a checklist of sustainable design rating systems,
which may lead to a box-ticking approach without embracing the
whole. This paper defined Key Performance Indicators for different
aspects of human health, wellbeing and energy in the urban context and
evaluated them in the context of a changing climate. This contribution
stresses the importance of connecting several critical performance indicators to adapt cities to climate change while enhancing human
health and connection to nature.
The need for target-oriented, regenerative and efficient urban
planning and design in cities is urgent due to relentless urbanisation.
While regenerative approaches are attracting increasing interest among
4.3. Integration of the workflow in digital design processes
The advantage of using parametric modelling means that it is possible to create design variations or climatic variations based on rulebased generation. The main benefit of this approach is the enormous
time savings compared to modelling in a non-parametric way and thus
the possibility to explore more design variants, which may lead to a
reduction of design costs and an increase in the spatial design qualities.
The performance-based computational design supports regenerative
analysis, synthesis and evaluation that mainly focuses on the
Fig. 19. Nature View Factor. The angular ratio between the number of lines of sight intercepted by tree canopies footprints and the total number of lines of sight at
each viewpoint (left). Minimal distance from the closest visible tree to each viewpoint (right).
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Fig. 20. A schematic view of the presentation of results overlaid on the geometrical models.
sustainability design practitioners, transitioning from energy-oriented
green building practices to a regenerative urban and building design
practice entails the challenges of incorporating new strategies, new
performance indicators, new methods and integrated tools.
Regenerative design requires blending several key performances into a
holistic thinking framework to see interrelationships between them.
Key to this is a digital environment, supporting informative decisions
and facilitating the exchange of information between stakeholders.
The application of this holistic approach is presented through a new
and multi-layered parametric workflow, based on data exchange and
synergies across different modelling tools. In addressing the issues
raised by this integration through the selection of appropriate parametric simulation scripts, visualisation and interaction models, this
paper introduces a prototype integrated parametric workflow. The
various components provide quantitative feedback to the designer
during decision-making. As the current software landscape is fragmented into single-function tools, this prototype workflow is necessary.
The workflow includes analyses and simulations in Rhinoceros 3D using
its parametric plug-in Grasshopper and the environmental plugins included in the Ladybug Tools suite. This workflow is applied to a specific
case study in Malaga, Spain, to explore its ability to quantify various
regenerative performance indices and is available in a unified tool
ecosystem to promote ease of usage.
The findings from this paper should improve the adoption of evidence-based urban design by helping designers make informed decisions. The value of this workflow is in making available, for the first
time, a diverse and extensive set of analysis tools for regenerative and
climate-appropriate design. The designers receive a spatial overview of
the urban configuration, as well as on performance in different future
scenarios and time frames. This can aid in the alignment of composition, layout and aesthetics to global climatic and ecosystem goals as
well as help ensure human wellbeing, in a holistic design framework.
Ultimately, the integration and interoperability demonstrated in
this paper will help increase the development and application of
parametric design studies and evidence-based optimisation of design
through easy-to-use quantitative tools. This article is a first step towards
providing the contemporary urban planning practice with clear goals
and interactive digital workflows supporting informed decisions and
facilitating the exchange of information between stakeholders. The results of this study are guidelines for urban designers to define the regenerative characteristics of cities and a workflow that supports key
choices in the design process. In future work, this would be extended to
a thorough treatment of the computational and conceptual impacts of
uncertainty resulting from the evolution of the climate, microclimatic
conditions, and urban development. Furthermore, the parametric
workflow can be automated with improved interconnectedness and
decision-making.
Acknowledgements
The article was developed with the support of the COST Action
CA16114 ‘RESTORE: Rethinking Sustainability towards a Regenerative
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E. Naboni, et al.
Economy”. The authors would like to acknowledge the help of Manuel
de-Borja-Torrejón in gathering the background data. The second author
gratefully acknowledges the financial support of the German Academic
Exchange Service (DAAD) for his PhD research grant. The fourth author
would like to acknowledge the SCCER – FEEB&D for providing research
funding and Prof. Jean-Louis Scartezzini for the scientific support.
Appendix A. La Luz, Malaga
The site is a polluted heat-island disconnected from sea breezes, with extensive hardscape (built-up area) and few natural elements. The wellbeing of the residents is further compromised due to modifications by tenants that obstruct natural ventilation and light. Climate change may
exacerbate these baleful outdoor and indoor conditions, stressing the overall need for scalable interventions. La Luz features 222 dwellings/ha and a
population density of 33.100 inhabitants/km,2 one of the most densely populated neighbourhoods in Malaga. The percentage of the unbuilt area is
62%, and the availability of green areas is scarce despite the presence of two gardens. The result is a monotonous and dull landscape, spread over
8620 m2 of gross floor area and containing 1190 apartments. The height of the buildings and the width of the road creates an oppressive space and
shaded areas with insufficient direct solar radiation for social interaction. The blocks should, by law, be spaced apart 1.5–2.5 times their height, but
are not.
Appendix B. Weather file generation input values
Table B1
Input Parameter
Selected Value
Representative Concentration Pathway (RCP)3
Random Seed
Sample years (used to create decadal TMY)
Number of samples (used to create decadal TMY)
8.5
42
2051–2060, 2071–2080
100 r decade
Appendix C. Albedo values
Table C1
Surface
Albedo
Corrugated roof
Colored paint
Trees
Asphalt
Concrete
Grass
0.15
0.35
0.18
0.2
0.4
0.25
Appendix D. CFD settings
The setup of the CFD simulations followed recognised best practices for outdoor wind simulations for all relevant simulation parameters [74].
The wind tunnel generated was 2.5 km long, 1 km wide, and 200 m tall. The model used was the realisable KE, a RANS variant that has shown
outstanding performance and convergence for outdoor studies. Simulations were run until the convergence criteria for all relevant parameters were
satisfied. A summary of all relevant input parameters can be found in Appendix F. All four simulations were created with Butterfly, which allows for
a rapid and accurate CFD case generation and runs with OpenFOAM engine.
Table D1
Input Parameter
Selected Value
Climatic Parameters
Wind Directions (degrees from North)
South East (135)
West (270)
West North West (292)
North West (315)
2.69
4.09
4.02
3.74
68.3
Reference Wind Velocity (m/s)
Wind Coverage (% of hours)
Simulation Parameters
Reference Height (m)
Ground roughness value
Boundary Conditions
Top and Sides
Outlet Surface
Inlet Surface
10
1.0 - Closed
Slip (zero gradients)
Zero Pressure
Atmospheric Boundary Layer Inlet Velocity
(continued on next page)
3
Representative Concentration Pathways have replaced the emissions ‘scenarios’ used in previous IPCC reports from Assessment Report 5 onwards.
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Table D1 (continued)
Input Parameter
Selected Value
Turbulence Model
Discretisation Schemes
Convergence Criteria
Mesh
Cell Size – Area of Interest (m)
Windward Extension (m)
Leeward Extension (m)
Sides Extension (m)
Height (m)
Cell to Cell Ratio
Wake Offset (m)
Height Offset (m)
Realisable-KE Model
2nd Order
10e-4 for all variables
4
400
1900
400
200
1.15
50
10
Appendix E. Energy Simulation Parameters
Table E1
Parameter
Value
Zone loads
Lighting
Schedule
Occupancy
Schedule
HVAC:
Heating/cooling setpoints
Schedule
Coefficient of performance (COP)
Ventilation
Schedule
Material properties:
Exterior Walls
Roofs
Exterior Floors
Windows
Infiltration
Floor Height
4.4 W/m2
Mon.-Sun.
10% 1–7, 30% 8–19, 100% 20–23, 50% 19,24
0.06 People/m2
Mon.- Fri (Weekends 100%) 100% 1–7, 25% 8–15, 50% 16–23. 100% 24
20°/25°
Heating: Jan-May, Oct-Dec
Cooling: Jun-Sep
2 (cooling), 0.98 (heating)
4 Air Changes per Hour (ACH)
Jun-Sep; 1-8
U = 0.94 W/m2K
U = 0.5 W/m2K
U = 0.53 W/m2K
U = 5.7 W/m2K, SHGC = 0.6
1.25 ACH
3m
Appendix F. Daylighting radiance settings
Table F1
Parameters
Description
Value
-ab
-aa
-ar
-ad
-as
context
Interior walls
Interior ceiling
Interior floor
Ambient bounces
Ambient accuracy
Ambient resolution
Ambient division
Ambient sample
reflectance
Reflectance
Reflectance
Reflectance
2
0.15
128
512
256
0.35
0.5
0.8
0.2
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