Renewable Energy 179 (2021) 2016e2035
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Renewable Energy
journal homepage: www.elsevier.com/locate/renene
From simulation to data-driven approach: A framework of integrating
urban morphology to low-energy urban design
Wei Wang a, Ke Liu a, Muxing Zhang b, Yuchi Shen a, Rui Jing c, Xiaodong Xu a, *
a
School of Architecture, Southeast University, Sipailou 2, Xuanwu District, Nanjing, 210096, Jiangsu province, China
School of Energy and Environment, Southeast University, Sipailou 2, Xuanwu District, Nanjing, 210096, Jiangsu province, China
c
School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 29 March 2021
Received in revised form
19 July 2021
Accepted 6 August 2021
Available online 11 August 2021
Energy-efficient urban design is an important prerequisite to sustainable urban development and
reduction of greenhouse gas emissions. This study proposes an automatic framework to optimize urban
design through the use of an urban building energy model. Three optimization goals were defined:
maximum solar energy utilization, solar lighting of the first floor, and minimum building energy demand.
Urban morphology was integrated into the optimization process as the bridge between the urban design
scenario and the actual urban block. To validate the model, this study abstracted basic urban forms from
actual urban contexts to generate urban blocks with the Rhino tool and run optimization in the Wallacei
X, for multi-objective optimization in Rhino. The long short-term memory (LSTM) network was applied
to infer energy performance of 41 actual urban blocks in Jianhu, China. In the results, the proposed
framework can be validated feasibly with optimization of 100 iterations. A set of optimal results will be
achieved for three goals and five clusters defined for different concerns of urban design strategies. The
LSTM can achieve the best accuracy of 1.21% and 1.37% for energy generation of photovoltaic and total
building energy use intensity respectively.
© 2021 Elsevier Ltd. All rights reserved.
Keywords:
Urban building energy model
Urban morphology
Urban design framework
Low-energy design
Solar energy utilization
1. Introduction
The urbanization process in China has expanded rapidly, and at
the end of 2019, the urbanization rate reached 60.60%, an increase
of 49.96% from 1949 with an average annual increase of 0.71% [1].
However, high-speed urban construction has brought huge challenges, resulting in a significant increase in energy consumption
and greenhouse gas (GHG) emissions. Cities consume more than
two-thirds of the world's primary energy and emit more than 70%
of global GHGs [2]. As important end-users, buildings account for
28% of global carbon emissions, and this number will continue to
grow in the future, especially in the vast developing areas of Asia
and Africa [3]. In recent years, many countries have set goals and
plans to reduce energy use and GHG emissions to achieve more
ecological and low-carbon urban development where green
building design at the micro level, green city design at the meso
level, and low-carbon city planning at the macro level are important means.
In recent years, urban building energy modeling (UBEM) has
emerged with the aim of providing technical support for energyefficient urban design, urban energy planning, and urban renewal
by providing spatial and temporal calculations of energy demand
and renewable energy utilization potential of urban buildings.
Meanwhile, as solar energy has become the feasible, popular, and
clear renewable energy source to reduce GHG emissions, more and
more researchers have started to explore energy-efficient urban
design to reduce energy demand as well as increase solar energy
utilization. With the benefits from UBEM, this study proposes an
automatic low-energy urban block design framework that utilizes
the urban energy simulation and data-driven approaches, both
popular means in UBEM, and integrates the long short-term
memory (LSTM) network algorithm to uncover the energy-saving
opportunities of actual urban blocks.
2. Background
2.1. Review on urban building energy modeling
* Corresponding author.
E-mail address: xuxiaodong@seu.edu.cn (X. Xu).
https://doi.org/10.1016/j.renene.2021.08.024
0960-1481/© 2021 Elsevier Ltd. All rights reserved.
An
urban
building
energy
model
mainly
includes
an
W. Wang, K. Liu, M. Zhang et al.
Renewable Energy 179 (2021) 2016e2035
network (ResNet) and physical models to simulate building energy
consumption on different time and space scales [15]. Roth et al.
proposed a new, enhanced urban building energy model (A-UBEM),
which combines data-driven and physics-based energy simulation
methods to generate hourly data for each building in the city [16].
engineering-based simulation model and a data-driven model. The
former establishes a physical simulation model of the building
group by simulating building energy consumption using a tool such
as EnergyPlus; displays the spatial distribution of building energy
consumption using the ArcGIS tool; and finally displays the temporal and spatial distribution of urban building energy. The datadriven model employs known data sets related to building group
energy use, such as energy utilizations, building physical characteristics, etc., to learn building energy use. To speed calculation, the
simplified physical model is generally adopted in UBEM [4]. Yang
et al. briefly reviewed the current urban energy modeling tools, and
proposed a way to integrate UBEM into urban design to realize the
concept of green and energy-efficient urban blocks by optimizing
the urban form [5]. Li et al. compared several urban energy simulation tools and took the Hangzhou South Railway Station area as
an example to evaluate the effectiveness and related challenges of
different tools [3]. Yan et al. proposed a method for building energy
estimation at the block scale with DeST, and validation cases show
that under the climate background of Urumqi, Beijing, and
Shanghai, the difference between the estimated annual heating
demand of block buildings and simulation is less than 6% [6]. When
real energy data of building blocks cannot be obtained, the
engineering-based simulation method is the best choice, which,
however, has very high requirements for the computing source and
time cost. Research [7] pointed out that for an energy simulation
model of a typical single commercial building with a calculation
resolution of 5 min, its annual energy consumption requires 10 min,
so that 10,000 buildings might require at least 69 days (not
counting the modeling time).
Data-driven methods are also popular for rapid energy assessment of urban blocks. Jovanovic et al. used three kinds of artificial
neural networks to predict the heating energy use of university
campuses: a feedforward back propagation neural network (FFNN),
a radial basis function network (RBFN), and an adaptive neurofuzzy interference system (ANFIS). The results show that all can
predict heating energy consumption very accurately [8]. Ma and
Cheng used multiple linear regression, artificial neural networks,
support vector regression (SVR), and a GIS integrated data analysis
framework to estimate the annual energy use intensity (EUI) of
3640 residences in New York City [9]. Kontokosta and Tull used
linear regression (OLS), random forest (RF), and SVR algorithms to
predict energy usage in cities. The results showed that when the
OLS model is extended to the entire city, the OLS model has the best
effect [10]. Ma et al. used cluster analysis to predict the daily
heating load distribution of educational buildings to determine the
typical daily load distribution and classify buildings based on these
distributions [11]. Tardioli et al. used random forest, k-means
clustering, and principal component analysis to build a machine
learning model to identify representative residential and commercial buildings and architectural clusters in the urban data set,
finally identifying 67 representative buildings [12]. Robinson et al.
used RF, SVR, and gradient boosting in the commercial building
energy consumption survey to predict the annual energy consumption of commercial buildings in New York City [13].
In the latest research, many studies have started to combine
engineering-based simulation with data-driven methods to obtain
building group energy consumption data, where the former applied
simulation data as the input for the latter's learning, which can
effectively overcome their own shortcomings. Dong et al. integrated a data-driven model into the physical model to predict the
hourly or one-day-ahead load of residential buildings, showing that
the hybrid model improves the prediction accuracy and reduces the
computational complexity of the traditional engineering-based
model [14]. Nukiewicz et al. proposed a data-driven urban energy
simulation (DUE-S) framework, which combines residual neural
2.2. Review on urban microclimate and building energy
Many studies have shown that urban morphology has an
important impact on building energy consumption through
microclimate, such as solar radiation, temperature, relative humidity, and wind speed. Temperature is considered to be one of the
most important microclimate factors that directly affect the heating
and cooling needs of buildings. Michele et al. found from the actual
climate measurement of a community in Rome, Italy, that
compared with rural areas, the number of heating days in urban
areas can be reduced by up to 18%, while the number of cooling
days can be increased by up to 157%. The heat island effect can
reduce the heating energy consumption of residential buildings by
up to 21% and the heating energy consumption of office buildings
by up to 18% [17]. Boccalatte et al. explored the temperature of
street canyons in different urban forms to assess the impact of the
heat island effect on building energy consumption. M'Saouri et al.
analyzed the influence of street layer gorge height-to-width ratio
on building surface temperature and surface absorption of radiation in Tangier, Morocco and found that compared with the single
building, the radiation absorption of the external wall of the street
layer gorge increases, leading to an increase in the surface temperature of the street layer gorge, which ultimately causes an increase in cooling demand in summer and a decrease in heating
demand in winter [18].
Solar radiation is also closely related to the energy demand of
buildings and is recognized as an important renewable energy
source; hence, its utilization at the urban block level has received
extensive attention. Vallati et al. investigated the impact of shortwave radiation on the heating demand of street buildings after
multiple reflections in the street gorge. They found that the narrower the street level, the stronger the radiation capture phenomenon, which leads to the increase in space cooling demand that
is much higher than the decrease in space heating demand [19]. Lee
et al. proposed four basic urban block models and explored the
relationship between different blocks and the potential of active
and passive solar energy utilization. The results also show that the
solar radiation accessibility is closely related to building density
[20]. Mohajeri et al. explored the relationship between various
compactness indicators and solar energy potentials in 16 communities (11,418 buildings) in Geneva, Switzerland, and evaluated the
potential of solar energy under different compactness in building
integrated photovoltaic (BiPV), solar thermal collector (STC), and
passive solar heating systems [21]. Xu et al. evaluated the photovoltaic potential of different urban block forms in Wuhan, China,
and the results showed that commercial blocks received the most
solar radiation, followed by residential blocks and finally industrial
blocks [22]. Liu et al. discussed the impact of densified urban
morphology on building energy consumption in hot summer and
warm winter regions in China, showing that in areas with hot
summers and warm winters, the cooling energy of enclosed
buildings can be reduced by 7e15% compared with open layouts
[23].
2.3. Review on urban morphology and energy modeling
Studying the urban microclimate, some researchers also proposed to directly analyze the relationship between urban form and
energy performance from the perspective of architecture and urban
2017
W. Wang, K. Liu, M. Zhang et al.
Renewable Energy 179 (2021) 2016e2035
design and tried to find optimal, energy-efficient and sustainable
strategies for urban block design. Nataniana applied ideal architectural prototypes to simulate the urban form under different floor
area ratios and Mediterranean climate conditions. The results show
that with the increase of the floor area ratio, the cooling energy
intensity of office buildings and residential buildings has shown a
downward trend [24]. Quan et al. conducted energy simulations on
some real blocks in Shanghai, China and found that the larger the
plot ratio of the block, the greater the EUI of the building [25]. Quan
et al. conducted a simulation on the urban blocks of Portland,
Oregon, and found that when the building density increases to a
certain level and then continues to increase, the building EUI begins
to decrease [26]. Oh and Kim extracted 13 urban morphological
factors, used regression analysis to explore the importance of
building energy performance, and used machine learning and
clustering methods to classify urban morphological factors [27].
Mangan et al. discussed the influence of temperate urban
morphology on building energy and cost efficiency and established
120 morphological models by considering five elements, among
which, building height and the aspect ratio have a greater impact
on energy use than does orientation [28]. Pan et al. studied the
influence of urban morphology on outdoor night lighting by
studying 11 urban communities in Shenzhen, China, and proposed
corresponding lighting layout optimization strategies from the
perspective of energy saving [29]. Li et al. used data mining technology to analyze the urban morphological factors affecting
building energy consumption based on an urban building data set
composed of 539 residential buildings and 153 public buildings and
found factors such as orientation, body shape coefficient, and
building perimeter ratio [30].
One research investigated energy use and building types of 4
cities in London, England; Paris, France; Berlin, Germany; and
Istanbul, Turkey and found that detached houses have the highest
heating energy consumption and little difference between the
heating energy consumption of high-rise houses, slab buildings,
and townhouses [31]. Vartholomaios studied the impact of three
building types in the Greek Mediterranean city of Thessaloniki on
heating and cooling energy, and the results show that compact
layout, south-facing buildings, and courtyard-style block forms are
the most energy-efficient [32]. Xie et al. proved that the courtyard
shape can reduce the heating demand and enhance the ventilation
and cooling capacity at night [33]. Pan et al. established a parametric model to study the energy of different building types in
Shanghai, China and showed that when the building density was
0.169, the courtyard-style building type showed the best energy
performance, followed by the plate type and point type [34].
simulations of which are run in the Rhino Ladybug module, and the
optimization is run in the Wallacei module, one plug-in tool in
Ladybug. In the next step, a machine learning algorithm is applied
to learn the performance of the real urban block to reveal the gap
between design and actual scenarios. In this step, LSTM was
selected as the predictive technique and the training dataset was
generated from the optimization process in Step 4.
3.2. Cases of urban blocks
The selected case city is Jianhu, a county-level city in Jiangsu
Province, China, which has a total area of approximately 1160 km2 and
a total population of approximately 0.8 million. The city is at an altitude of approximately 2 m, and its latitude and longitude are from
33160 to 33 410 and from 119 330 to 120 050 , respectively. It has a
typical subtropical climate (Cwa) according to the KoppeneGeiger
climate classification. As shown in Fig. 2, the dataset in this study
includes 539 residential buildings from 42 residential block communities. The dataset was obtained from the official Department of
Power Supply in Jianhu. The information of these buildings comprises
their address, year built, footprint, geometry (area, length, width, and
height), and energy usage for the year 2018. Jianhu is located in a
medium-type area of solar energy resources, which is suitable for the
development of solar energy utilization. Its average southward vertical solar radiation illuminance is 60e70 W/m2.
Based on the study of the actual road grid scale in Jianhu, this
study selects a 240 m 240 m block as the ideal one, which is
further divided with a 3 3 grid to generate 9-unit plots of
80 m 80 m (seen in Fig. 3). The boundary line of the unit land is
offset inward by 5m, which is regarded as the boundary line of the
road inside the block. 5m is offset to the inside of the unit land as a
building boundary line. The construction range of each unit land is
60 m 60 m, and it is stipulated that only one building type unit
can be placed.
3.3. Urban morphology
3.3.1. Basic urban type and morphology abstraction
This study extracts a total of 9 building types, as presented in
Table 1, which are common in the urban area of Jianhu, from 3
categories, of which 4 are point building types, labeled as P-1~P-4.
There are 3 panel building types, labeled respectively as S-1~S-3;
there are 2 types of courtyard-style buildings, labeled as C-1 and C2, respectively. It should be noted that the type and the height of
the building are closely related to the density of the building. The
building type determines the different upper limit of the building's
height. This study divides the height interval of the building type
into three sections: 1 to 3 floors, 4 to 12 floors, and 13 to 30 floors.
As buildings above 30 floors are often or close to super high-rise
buildings above 100 m, which must be fully demonstrated and
strictly in small and medium-sized cities, buildings above 30 floors
are not discussed in this study. Each height interval contains three
building types. In order to facilitate the calculation of building area
and data processing and analysis, the floor area and building density of the building types within the same height interval are supposed to be the same, the details of which are shown in Table A1.
Once the urban block design is set up, several urban morphological parameters can be defined to describe the block, which also
correspond highly to energy demand and production. The
morphological parameters in this study include orientation (OR),
floor area ratio (FAR), building density (BD), average number of
floors (AF), scattered degree (SD), height to width (HW) from four
directions, open space ratio (OSR), shape coefficient (SC), and
perimeter-to-area ratio (PAR). Their definitions are introduced in
Table A2, while their calculation methods are included in Table 2.
3. Methodology
3.1. The framework of this study
Fig. 1 illustrates the main framework of low-energy urban
design integrating urban morphology to urban design using
simulation tools and a data-driven approach. In Step 1, the framework creates an ideal urban block to test and validate the lowenergy design flow, and it also creates the basic training dataset
of morphological parameters and energy for data-driven learning.
Assisted with real urban block data, Step 1 abstracts several basic
urban forms as in Fig. 1. In Step 2, this framework generates ideal
and real urban blocks in Rhino and identifies the controllable factor
for constructing the optimization, while Step 3 provides the
quantitative indices of urban morphology. Step 4 proposes the
optimization process of urban design with the three goals of
maximum energy generation of PV, maximum solar hours for first
floors of buildings, and minimum building energy demand,
2018
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Renewable Energy 179 (2021) 2016e2035
Fig. 1. Low-energy urban design framework for integrating urban morphology to urban design from simulation to a data-driven approach.
module, and data recording module, which are presented in Fig. 4.
This study firstly determined the minimum building energy use
intensity, the maximum total solar PV power generation in the
block, and the maximum average sunshine duration on the ground
floor of the block as the optimization goals. Second, building type,
building height, open space location, block orientation were
3.3.2. Urban design scenario and optimization
An automatic optimization experiment of ideal block
morphology driven by energy performance under the condition of
variable floor area ratio was organized, which included five modules: a parameter preset module, block generation module, performance calculation module, multi-objective optimization
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Renewable Energy 179 (2021) 2016e2035
Fig. 2. Location and layout of Jianhu and distribution of the available buildings in this study (red: public building; yellow: residential building).
Fig. 3. Illustration of an ideal urban block.
Wallacei X is the key built-in and integrated NSGA-II (Non-dominated Sorting Genetic Algorithm II), one multi-objective optimization algorithm, which has been widely applied in optimization
cases [35]. In Wallacei, since the plug-in default settings are optimized in the direction of gradually decreasing the target value,
while the solar photovoltaic power generation and the average
sunshine duration corresponding to the block shape are expected
to gradually increase, these two objectives are processed as negative and then connected to the wallacei plug-in in Rhino. The data
record module TT toolbox was used to record the outputs of optimization process and descriptive morphology factors. In this study,
the objectives can be described in Eqs. (1)e(3) and Eq. (4) concludes
the multi-objective functions.
considered the controlling morphological factors. Third, the
descriptive morphological factors and site environmental parameters used in this study were selected to construct a block
morphological parameterized model. Next, site weather data and
other energy simulation settings were determined, and building
energy use, solar hours, and PV generation were calculated on each
urban block model. Finally, the Pareto optimal solution was automatically launched according to the genetic setting, and the data
recording plug-in simultaneously recorded the morphological factors and simulation results of each urban block. In the urban block,
one of the nine units was used as an open space, while the
remaining eight units of land could be freely placed with different
heights and types of buildings. The simulation range was the whole
year, the solar calculation time was 8:00e16:00 on a cold day, the
building floor height was set to 3m, the population size of the
optimization algorithm was set as 33, and the number of iterations
was 100 generations.
With input on the site environment and controllable urban
morphological factors, the urban block was created in the Rhino
tool. The Ladybug and Honeybee plug-ins in Rhino were applied to
simulate the solar radiation, solar hours, and building energy
simulation. One note for energy simulation is that this study
applied the Dragonfly plug-in to simulate a micro-climate, which
depends highly on the morphology of the urban block, to modify
the EPW weather file and provide a local weather file for energy
simulation. The optimization tool, Wallacei platform 2.5 (https://
www.wallacei.com), was introduced for this process, for which,
max fSolar radiation ¼
n¼9
X
gi ðbti ; bfi ; osi ; Oi Þ
(1)
i¼1
max fSolar hour ¼
n¼9
X
hi ðbti ; bfi ; osi ; Oi Þ
(2)
i¼1
min ftoal EUI ¼
n¼9
X
i¼1
2020
li ðbti ; bfi ; osi ; Oi Þ
(3)
W. Wang, K. Liu, M. Zhang et al.
Renewable Energy 179 (2021) 2016e2035
Table 1
Basic urban building form abstraction and classification.
Max F ¼ ½fSolar radiation ; fSolar hour ; ftoal EUI T
Algorithm 1. In the beginning, N solutions are randomly sampled in
the search space to form the initial population P. Their objectives, as
well as the nondominated levels and crowding distances, are then
(4)
s:t: bti 2½P 1; S 1; C 1; P 2; S 2; C 2; P 3; P 4; S 3
bfi 2½3; 6; 9; 12; 18; 24; 30
osi 2½0; 1; 2; 3; 4; 5; 6; 7; 8
Oi 2½ 45 ; 30 ; 15 ; 0 ; 15 ; 30 ; 45 (5)
computed (line 1e2). Using a fast nondominated sorting approach,
solutions in P are divided into different nondominated levels,
where solutions in the same level are nondominated with each
other and a solution in a higher level is dominated by at least one
solution in its next lower level. During the selection process, solutions with lower nondominated levels and larger crowding distances are preferred to preserve convergence and diversity. In each
generation, N child solutions are generated by applying crossover
and mutation operators on parent solutions selected by a binary
tournament selection operator according to their nondominated
levels and crowding distances (line 4e9). Given the union of the
current population and child solutions, N solutions with lower
where fSolar radiation , fSolar hour , ftoal EUI are three objectives, maximizing solar radiation and solar hour, and minimizing total EUI. The
constant, 9, is the number of the designed blocks, referred to Fig. 3.
bti ; bfi ; osi ; Oi are building type, building floor, open type, and
orientation, respectively. The building type and building floors can
be referred to Table A1, while the orientation greater than 0 means
south to east; less than 0 , it means south to west; 0 means
orientation is due south.
The pseudo code of NSGA-II used in this paper is presented in
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Renewable Energy 179 (2021) 2016e2035
Table 2
Abstraction and calculation of urban morphological parameters in this study.
OR
FAR
BD
Pn
OR ¼
Pn
i¼1 ORi
FAR ¼
Pn
i¼1 Si
BD ¼
As
i¼1 fi
As
AF
SD
Pn
Si
AF ¼ Pi¼1
n
i¼1 fi
SD ¼ hmax ha
n
HW
HW ¼
Pn
Hi
i¼1
Wi
OSR
SC
PAR
P
As ni¼1 fi
OSR ¼
Pn
i¼1 Si
Pn
c
SC ¼ Pni¼1 i
i¼1 hi fi
Pn
pi
PAR ¼ Pi¼1
n
i¼1 fi
Note: n is the number of buildings; is the orientation of building i; Si is the total floor area of building i; As is the area of block; fi is the floor area of building i; hmax , ha are the
maximum and average building height of the block; Hi , Wi is the height and width difference of the Canyon. ci is the surface area of building i; pi is the perimeter of building i.
Fig. 4. Optimization process of urban design under three goals.
3.4. Urban building energy simulation tuning
nondominated levels and larger crowding distances are selected to
form the new population in the next generation (line 10e12).
In order to facilitate comparison of the energy performance of
different blocks, the same prototype and preset parameters were
adopted for the entire block. These referred to the design standard
Algorithm 1. NSGA-II
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Renewable Energy 179 (2021) 2016e2035
Table 3
Energy simulation tuning for the urban block.
Parameter
Simulation period
Internal load
HVAC system
Heat transfer coefficient
Settings
From Jan.1 to Dec. 31
5 W/m2
1.9 W/m2
0.03 ppl/m2
Heating: Dec. 1 to Feb. 28, 26 C
Cooling: Jun. 15 to Aug. 31, 18 C
1
30 m3/(h$person)
K ¼ 0.5 W/m2$K
K ¼ 0.8 W/m2$K
K ¼ 1.5 W/m2$K
K ¼ 2.70 W/m2$K(SHGC ¼ 0.78; VT ¼ 0.55)
Lighting
Equipment
Occupants
Heating/cooling range and temperature
ACH
Mini. fresh air
Roof
Wall
Floor
Window
3.5. Long short-term memory network (LSTM) approach
for energy efficiency of residential buildings in hot summer and
cold winter zones JGJ 134e2010 [36] and the designcode for heating, ventilation, and air conditioning of civil buildings (GB 50736-2012) [37]. The air-conditioning cooling season
was set from June 15th to August 31st and the heating season from
December 1st to February 28th. The indoor heating temperature in
winter was set to 18 C, while the indoor cooling temperature in
summer was set to 26 C. The number of air changes per hour was
1.0 (i.e., 1ACH), which means that indoor air was replaced by fresh
outdoor air every hour. The minimum fresh air volume per capita
was 30 m3/(h$person), and an ideal air-conditioning system was
adopted. The specific parameters are shown in Table 3.
Photovoltaic power generation was calculated based on the
amount of solar radiation on the surface of the PV power generation
panel. Considering that the large-scale solar energy utilization of
building facades might have an impact on the city's appearance,
only the use of rooftop solar energy was considered in this study.
The threshold value of roof solar PV power generation in this study
was set as 800 kWh/m2$y [38]. The polycrystalline silicon PV
modules are supposed have high power generation efficiency and
poor light transmission. The photoelectric conversion coefficient is
generally between 10% and 18.5%, therefore, it was set to 17%. The
conversion efficiency of direct current to alternating current was
set to 85%. Since the actual building roof cannot be used to install
solar photovoltaic panels, the useable rate of the roof area was set
to 90%. Actual installations of rooftop PV panels are often not placed
parallel to the roof but have a certain inclination. Since this study
focuses on the theoretical PV capacity potential comparison of
different block shapes, rather than precise energy production, the
inclination angle was not considered.
An LSTM was adopted for a time-series dataset as it contains the
cell state to memorize long-term dependencies. The cell state units
transfer in formation backwards and forwards such that earlier
information learned from a sequence can influence the prediction
of later input. This information dissemination is crucial for timeseries meteorological data from varying input time points, which
is highly suitable for this study. The information that is stored,
removed, modified, and passed around is controlled by gates as
shown in Fig. 5. The mapping iterative calculation between the
input and output sequence is shown in the following Eqs. (6)e(10)
[39,40],
it ¼ sðW xi xt þ W hi ht1 þ W ci ct1 þ bi Þ
(6)
ft ¼ s W xf xt þ W hf ht1 þ W cf c t1 þ bf
(7)
ct ¼ it 1 tanhðW xc xt þ W hc ht1 þ bc Þ þ f t 1c t1
(8)
ht ¼ ot 1tanhðc t Þ
(9)
ot ¼ sðW xo xt þ W ho ht1 þ W co c t þ bo Þ
sðxÞ ¼
1
ð1 þ ex Þ
(10)
(11)
In the formulas, it , ft and ot stand for the activation vector of the
input gate, forget gate, and output gate. The ct represents cell state
Fig. 5. Architecture of LSTM network.
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W. Wang, K. Liu, M. Zhang et al.
Renewable Energy 179 (2021) 2016e2035
vector, and the ht represents the hidden state vector (output of
LSTM unit). In each term, W is the weight matrix while b is the bias
term. The sigmoid activation function is presented in Eq. (11).
4. Results
4.1. Optimization results of urban block design process
In this study, the operation was performed on a Windows 10
system (i7-9700, 8 cores, 3.00 GHz, 32G memory), taking nearly
240 h, and iterated for 100 generations, until the optimization
target value converged to a stable state. The optimization experiment produced 297 sets of non-dominated solutions, and 136 sets
of non-dominated solutions were obtained after removing the 161
sets of repeated solutions, including a total size of 4821. Calling the
calculator in the Wallacei analytics section can visualize the change
trend of the optimization goal during the optimization operation
process.
Fig. 6 is the spatial distribution diagram of the solution set
composed of all feasible solutions in the optimization process. Each
small green cube in the space represents a feasible solution in each
iteration. Each green cube connected by the red straight line and
Fig. 6. Solutions of the optimization algorithm for three goals. (X-Axis: energy production of urban block, Y-Axis: building energy use intensity of urban block, Z-Axis:
avg. solar hours of urban block.)
Fig. 7. Trend of different goals in the optimization.
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optimization process, starting from the average power generation
of the first generation of 1.76 106 kWh, the average solar PV
power generation of each generation of blocks has increased
rapidly. When the iteration enters 63 generations, the average
value of power generation tends to be stable about 2.27 106 kWh.
Inferred from Fig. 7(c), when the curve changes from red to blue,
the optimization keeps moving to the left. The curve does not
change much, but the slope gradually decreases, indicating that the
EUI in each iteration increases and there is a bigger difference from
the average. Fig. 7(d) shows that from the 1st to the 18th generation, the average value of the total EUI of the block experienced a
sharp decline, from 73.8 kWh/m2 to 70.4 kWh/m2, and then from
the 19th to the 66th generation, the average value of the total EUI of
the block rose steadily and finally stabilized after 67 generations,
fluctuating around 71.6 kWh/m2. Inferred from Fig. 7 (e), the
average solar hours of the block are increasing in each iteration.
From Fig. 7 (f), it can be clearly observed that from the 1st to the
22nd generation, the average sunshine duration of the block
increased rapidly, from 4.7h to 6.84h, until stable results of about
6.9h.
crossed by the red space surface represents the non-dominated
solution calculated in this optimization process (i.e., the Pareto
optimal solution). The closer the cube is to the origin of the threedimensional coordinate, the greater the power generation, the
better the lighting performance, and the greater the total energy
consumption of the block. It can be seen from Fig. 6 that the nondominated solution set is distributed at the forefront of the overall feasible solution, closest to the origin of the coordinate, indicating that compared with other feasible solutions generated in the
optimization process, the performance of the non-dominated solution is better.
The iterative trend of the three goals is shown in Fig. 7. Since the
multi-objective algorithm uses gradual reduction as the default
optimization direction, the solar PV power generation and average
sunshine duration was set as the negative value in the optimization,
so (a), (b) and (e), (f) respectively represent the changing trend of
its negative value. In Fig. 7, (a), (c), and (e) are the standard deviation change trends of the corresponding target. Each curve represents the standard deviation of the results of 33 sets of
experimental targets in one iteration. The wider the curve, the
larger the difference between most of the target values and the
average value in this iteration; the steeper the curve, the smaller
the difference between most of the target values and the average
value. Fig. 7 (b), (d), and (f) are the trend graphs of the average value
of the corresponding target. Each value represents the average
value of the 33 groups of experimental target results in one
iteration.
From Fig. 7(a), curves from red to blue continue to move to the
left, indicating that the solar PV power generation of the block
continues to increase as the iteration progresses, but the blue curve
spans a larger and gentler slope. This indicates that in the later iterations, the target value of different experiments has a strong
degree of dispersion, which is quite different from the overall
average. It can be clearly seen from Fig. 7(b) that along with
4.2. Results of urban block design optimization
Fig. 8 presents the Pareto optimal solution results of three goals
and their distribution. The annual solar PV power generation of the
block is mainly concentrated in the interval above
2.1 10e6kWh$y, showing that most non-dominated solutions
have good PV power generation. The total EUI of buildings is mostly
evenly distributed around 70.4 kWh/m2$y, while the value of solar
hours is concentrated around 7.14h. Also, a high correlation coefficient of 0.935 can be found between EUIs of buildings and annual
solar PV power generation. The reason may be that, on the one
hand, to increase the power generation of the block, multi-story
buildings or low-rise buildings with a larger roof area were
Fig. 8. Histogram and correlation analysis of three optimization goals.
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well as improve internal natural ventilation of the block, thereby
reducing energy use of the buildings.
Generally, a large number of multi-story buildings appear in the
non-dominated solution, which is conducive to increasing the
output of PV power generation on the rooftops of the block. Highrise buildings are concentrated on the 0e5 sites, of which the 1 and
2 sites are more realistic, indicating the layout of high northwest
and low southeast is more conducive to energy generation. On one
hand, this layout can make most areas of the block have better
lighting. On the other hand, the dominant southeast wind in Jianhu
in summer can directly enter the center of the block and take away
heat as a cooling effect. At the same time, high-rise buildings
located in the west can provide shadows to reduce the heat gain of
the strong western sunshine in summer, while the northwest highrise buildings can also prevent wind in winter.
In Fig. 9, only orientations of 15 and 0 are included in the
non-dominated solution with the frequency of 111 and 25, showing
the better performance of the blocks with these two orientations
on the optimization goal. Generally speaking, the south is also the
optimal direction that can receive the maximum solar radiation in
hot summer and cold winter areas. It can be seen that in the nondominated solution, the building types of the site in the east row
numbered 6, 7, and 8 are mainly low-rise panel and point buildings,
and the sites 0, 1, and 2 of the west rows are mainly point buildings.
The central site No. 3 is dominated by low-rise panel buildings, No.
4 is dominated by high-rise point buildings, and No. 5 is dominated
by slab and courtyard buildings.
In order to quickly classify the 136 groups of non-dominated
solutions and extract representative block morphology, this study
performed K-means clustering algorithm in Wallacei's module for
selected in the optimization; however, these two types of buildings
often require higher energy demand. On the other hand, the optimization algorithm tried to reduce the scattered degree of the block
building to increase the power generation, which also reduced the
inter-building shading and increased building energy use.
The correlation coefficient between the average solar hours of
the block and the annual solar PV power generation is 0.398 as
showing in Fig. 8, indicating that there is a low negative correlation,
which might be attributed to the fact that both are closely related to
the mutual shading between buildings. Abstractly, reducing the
shading will improve the overall lighting performance of the
building and the solar PV power generation capacity of the roof.
This could be explained by the fact that, on the one hand, reducing
the inter-building shading has a more obvious effect on lighting
improvement of the upper floors, not for the first floor. On the other
hand, it may be because the optimization algorithm tends to select
low-rise and multi-story buildings with high power generation
output, but the bottom lighting of this type of building is poor,
especially in the courtyard type. The same negative correlation
coefficient with similar causes could be found for total EUI and solar
hours, which is 0.365.
In the non-dominant solutions, only No. 3, No. 4, and No. 5 sites
were selected as the layout position of the open space with frequency of 71, 60, 5, respectively, showing these 3 lands as open
spaces can obtain better optimization. Noting that the No. 3, No. 4,
and No. 5 sites are all located in the middle, especially the No. 4 site.
The central open space plays an important role in reducing the EUI
of buildings in the block, improving the building lighting and
increasing PV power generation. The central open space can also
greatly reduce the possibility of heat concentration in the block as
Fig. 9. The distribution of controllable parameters in non-dominated solutions.
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Fig. 10. Illustration of the clustering distribution of non-dominated solutions.
Table 4
Clustering results of non-dominated solutions.
Cluster
Clustering
Clustering
Clustering
Clustering
Clustering
Dataset
1
2
3
4
5
22
26
37
15
36
Centroids of Clustering
Power generation of PV(10e6kWh/y)
Building EUIy (kWh/m2/y)
Avg. solar hours (h)
1.8246
2.43
2.6345
2.5014
2.0987
68.91681
71.5294
74.15861
72.46578
70.84261
6.89088
7.19178
6.90196
6.50656
7.31951
there are available feasible solutions for urban design, all of which
are presented in Fig. A1 to Fig. A5. Table 5 gives the examples of
urban design and randomly selects the single-objective optimal
scheme and five cluster centroids in the non-dominated solution
set, as well as the single-objective worst scheme in feasible
solutions.
non-dominated solutions. With repeated comparison, when the
number of clusters K is 5, the classification effect is better. Fig. 10
shows the spatial map of the non-dominated solution set after
clustering analysis, with different colors. The black line connects
centroids of clustering and other non-dominated solutions.
As in Table 4 and Fig. 10, according to the optimization goals of
solar PV power generation (x) and the total EUI of the block
building (y), cluster 1 and cluster 3 are located at the end of the
overall non-dominated de-distribution, where clustering 1 has the
lowest total EUI and the lowest solar PV power generation, while
cluster 3 has the highest solar PV power generation and the highest
EUIs; however, the performance of clusters 2, 4, and 5 is more
balanced, and from cluster 5, 2, to 4, the EUI of the block is
increasing as well as the PV power generation. With regard to the
average number of solar hours, cluster 5 performed the best, cluster
4 performed the worst, and clusters 1, 2, and 3 performed more
evenly. Fig. 11 exhibits the differences in the performance of the
optimization goals. For each cluster of non-dominate solutions,
4.3. Energy prediction results of actual urban block
For this subsection, an LSTM algorithm was applied to learn
energy performance of 41 actual urban blocks (excluding 1 invalid)
through urban morphology. In the parameter tuning, the range of
number of hidden layers was [72, 72*2, 72*5], the epochs range was
[200, 500, 1000], and the learning rate was [0.005, 0.01, 0.02]. The
training loss and root mean square error was recorded in the
learning process to determine the parameter of LSTM. Seventy
percent of the dataset was used for training and 30% (data size was
1447) for validation. Finally, the number of hidden layers, the
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Fig. 11. Results of energy generation of PV, total EUI of buildings, and solar hours in five urban design clusters.
the feasibility of the framework, the Rhino tool was used to
generate an urban block with the basic urban form extracted from
an actual urban context, and automatic optimization was run with
three goals: maximum solar radiation and solar hours of the first
floor and minimum building energy demand. The LSTM network
was applied to infer the energy performance of actual urban blocks.
The results show that a set of optimal results will be achieved for
the three goals, and five clusters have been defined for different
concerns of urban design strategies. Although different optimization goals are in conflict, a balance can be found.
Urban building energy modeling has been an inspiring and
important approach that has received much attention in recent
years. With its help, this study proposed a framework of integrating
simulation and data-driven methods for low-energy urban design.
The former was applied in the design stage to find the optimal
urban design scenarios under pre-set goals, including maximum
solar energy utilization and solar hours, and minimum energy demand of buildings. The latter predicts the performance of the actual
urban block to reveal the gaps of energy use. Therefore, the main
contributions of this framework exist in (a) providing an automatic
way to achieve optimal low-energy urban design as well as maximize solar energy utilization and (b) creating a feasible way to
create the training dataset of urban morphology, energy demand,
and solar energy for a machine learning algorithm to analyze other
urban design or actual cases.
From the generations of urban design optimization in Fig. 7, one
can find a set of results for all goals available in urban design rather
than one best optimization. This study also clustered all the feasible
solutions in terms of three goals and provided urban designers with
means to maximize solar energy utilization or reduce energy demand. Those can also facilitate the real application of the proposed
framework to provide different reference scenarios in urban design.
Although the framework was introduced from design (an ideal
urban block) to practice (an actual urban block), the reverse is also
feasible, even more scientific and reasonable, to provide technical
support for new urban design or urban renewal projects from urban big data.
maximum epoch, and the learning rate were 72, 1,000, and 0.01,
respectively, with training time of 7646s. Table 6 shows the accuracy of the validation results and five outputs of LSTM; the area of
solar energy utilization (accumulated area with solar radiation
higher than 800W); the EUI of heating, cooling, and total buildings;
and energy generation of the PV panel were tested. It is clearly
found that LSTMs can achieve good accuracy in all predictions.
Among them, the prediction of energy generation and total EUI of
buildings have the best accuracy, with MAPE of 1.21% and 1.37% and
RMSE of 6.88 and 1.64, and the results of average prediction and
ground truth are validated well.
With high accurate validation of prediction algorithm, this study
learned the actual energy use of actual urban blocks using LSTM
through the its morphology defined in Table 2. To roughly compare
the predicted energy demand, actual energy use, and energy generation of PV, a COP of 4 (generally from 3 to 5) was selected to
transfer the building heating and cooling load to electricity use.
Fig. 12 presents the comparison results and indicates that in most
urban blocks, the predicted energy demand, which is also recognized as designed energy demand, is higher than actual energy use.
In a real urban context, the operative energy of buildings could be
determined and influenced by many factors, especially owners'
behaviors [41], although the vacancy rate in urban blocks is also
very high in China. However, those buildings, which have much
higher energy use than predicted energy demand, remain great
potential sources of energy savings. Comparing energy generation
and actual energy, the energy generation of most blocks can't
satisfy energy demand and actual use, however, which can
neutralize electricity from the grid.
5. Conclusion and discussion
The main work of this study is to propose a framework of
automatic low-energy urban design from simulation to data-driven
technologies in urban building energy models. In the urban design
framework, this study integrated urban morphology into the
optimization process, which can also be a bridge from an urban
design scenario to an actual urban block, or vice versa. To validate
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Table 5
Examples of urban design considering optimization of energy generation (EG, 10e6 kWh$y), total EUI of buildings (kWh/m2$y), and solar hours (h).
FAR
EG
EUI
2.19
1.81
Cluster
H (h)
68.81 min.
1
6.87
FAR
EG
EUI
1.88
1.82
Cluster
H (h)
68.92
1
6.89
FAR
EG
EUI
1.30
2.43
Cluster
H (h)
71.53
2
7.19
FAR
EG
EUI
0.89
1.45 min.
Cluster
H (h)
70.09
3
5.09
FAR
EG
EUI
0.89
2.65 max.
Cluster
H (h)
75.33
3
6.88
FAR
EG
EUI
1.15
2.50
Cluster
H (h)
72.47
4
6.50
FAR
EG
EUI
1.51
2.10
Cluster
H (h)
70.84
5
7.32
FAR
EG
EUI
1.56
2.01
Cluster
H (h)
70.76
5
7.53 max.
FAR
EG
EUI
2.86
1.98
Another solution
H (h)
5.48
78.39 max.
Table 6
Validation results for area of solar energy utilization (m2), heating EUI (kWh/m2$y), cooling EUI (kWh/m2$y), total EUI of buildings (kWh/m2$y), energy generation of PV
(kWh$y).
MAPE
RMSE
Avg. prediction
Avg. ground truth
Area
Heating EUI
Cooling EUI
Total EUI
Energy generation
3.32%
499.51
12463.64
12732.90
5.27%
1.10
14.73
15.14
2.08%
0.75
32.85
32.74
1.37%
1.64
72.90
73.20
1.21%
6.88
164.17
163.94
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Fig. 12. Results of energy generation and energy demand from prediction and actual energy use for 41 urban blocks.
Declaration of competing interest
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Table A1
The building forms abstracted from urban context in Jianhu County.
Type
Area (m2)
Building density
Floors
FAR
P-1
Point
2000
31.3%
1e3
0.31e0.94
S-1
Panel
2000
31.3%
1e3
0.31e0.94
C-1
Courtyard
2000
31.3%
1e3
0.31e0.94
P-2
Point
1500
23.4%
4e12
0.94e2.81
S-2
Panel
1500
23.4%
4e12
0.94e2.81
No.
Schematic diagram
Plane diagram
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Renewable Energy 179 (2021) 2016e2035
Table A1 (continued )
Type
Area (m2)
Building density
Floors
FAR
C-2
Courtyard
1500
23.4%
4e12
0.94e2.81
P-3
Point
1000
15.6%
13e30
2.03e4.69
P-4
Point
1000
15.6%
13e30
2.03e4.69
S-3
Panel
1000
15.6%
13e30
2.03e4.69
No.
Schematic diagram
Plane diagram
Table A2
The definitions urban morphological factors selected in this study.
Urban
density
Urban
density
Urban
morphology
Definition
Floor area ratio
(FAR)
Building density
Open space ratio
Generally used to describe the development intensity as one of the important planning indicators. It is equal to the ratio of the sum of the
total building area to the land area.
The ratio of the area of the building to the area of the construction land
The outdoor open space area per unit building area in the land which is equal to the ratio of the outdoor open space to the total building
area of each building
Refers to the avg. number of building floors.
Avg. number of
floors
Orientation
Scattered degree
The average orientations of all buildings
Describing the distribution characteristics in the vertical direction, which is equal to the difference between the maximum building height
and the average height in the land
Height to width Avg. value of street height to width ratio
Shape coefficient The ratio of the external surface area of the building to the volume of the building, used to evaluate the compactness of the building form
Perimeter to area The ratio of the sum of the floor area of the building to the sum of the perimeter.
ratio
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Fig. A1. The feasible urban design in no-dominate solutions from optimization in Cluster 1.
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Fig. A2. The feasible urban design in no-dominate solutions from optimization in Cluster 2.
Fig. A3. The feasible urban design in no-dominate solutions from optimization in Cluster 3.
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Fig. A4. The feasible urban design in no-dominate solutions from optimization in Cluster 4.
Fig. A5. The feasible urban design in no-dominate solutions from optimization in Cluster 5.
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Acknowledgement
[19]
The work described in this paper was sponsored by the National
Natural Science Foundation of China (NSFC #51978144) and the
Natural Science Foundation of Jiangsu Province (#BK20190362).
This work is also supported by “the Fundamental Research Funds
for the Central Universities” (#2242021k10006). Any opinions,
findings, conclusions, or recommendations expressed in this paper
are those of the authors and do not necessarily reflect the views of
the organizations.
[20]
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