Energy and environmental analysis of urban environment: methodology and application of an integrated approach

Abstract

The new vision of Smart Cities drives policies regarding sustainability of urban environment in terms of reducing consumption, environmental impacts and improving energy efficiency and safety of building sector. The mutual interaction between buildings and the surrounding built environment can affect all these fields. In particular, the paper investigates this interaction in order to analyse the energy behaviour of buildings at urban scale and the effect on the outdoor comfort. To reach this goal a multi-level and multi-purpose model was developed based on a synergic and integrated bottom-up approach for both energy efficiency and outdoor comfort analysis. The energy analysis is based on the identification of reference buildings with known energy performances, defined sing information available at Public Authorities, statistical studies and data from field surveys. A multi-level parametric approach used for the outdoor comfort analysis allows to identify the Universal Thermal Climate Index (UTCI) and the related stress level of the district starting from the environmental data available at rural station. The model allows the estimation of the current energy and environmental behavior of the built environment and identifies possible renovation scenarios.

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Energy and environmental analysis of urban environment: methodology
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IOP Conf. Series: Materials Science and Engineering 609 (2019) 072018
IOP Publishing
doi:10.1088/1757-899X/609/7/072018
1
Energy and environmental analysis of urban environment:
methodology and application of an integrated approach
F Salamone1, L Belussi1, L Danza1, Antonello Di Nunzio2, M Ghellere1, I Meroni1,
1 Construction Technologies Institute - National Research Council of Italy (ITC-CNR), Via Lombardia
49, 20098, San Giuliano Milanese, Milan, Italy
2 ENVI-MET GmbH, Kaninenberghöhe 2, 45136 Essen, Germany
* belussi@itc.cnr.it
Abstract. The new vision of Smart Cities drives policies regarding sustainability of urban environment in terms
of reducing consumption, environmental impacts and improving energy efficiency and safety of building sector.
The mutual interaction between buildings and the surrounding built environment can affect all these fields. In
particular, the paper investigates this interaction in order to analyse the energy behaviour of buildings at urban
scale and the effect on the outdoor comfort. To reach this goal a multi-level and multi-purpose model was
developed based on a synergic and integrated bottom-up approach for both energy efficiency and outdoor
comfort analysis. The energy analysis is based on the identification of reference buildings with known energy
performances, defined sing information available at Public Authorities, statistical studies and data from field
surveys. A multi-level parametric approach used for the outdoor comfort analysis allows to identify the
Universal Thermal Climate Index (UTCI) and the related stress level of the district starting from the
environmental data available at rural station. The model allows the estimation of the current energy and
environmental behavior of the built environment and identifies possible renovation scenarios.
1. Introduction
Cities play a key role in sustainability policies aimed at reducing fuel consumption and environmental
impacts and increasing energy efficiency in the building sector and improving human well-being
within the built environment. The interactions between buildings and the built environment are studies
since decades under different perspectives, from architectural [1] to engineering [2], from energy [3]
to climatic [4] point of view. Due to these complexities, urban policies have to consider several
characteristics related to buildings and the built environment as to provide the most suitable solutions
following a holistic approach: in this paper the attention is paid on energy and outdoor thermal quality
issues. In the last years a discipline aimed at assessing the performance of buildings at urban scale has
been consolidated [5]. Urban Building Energy Models (UBEM) are frequently used for estimating
the energy–saving potential in the building stock [6]. These models analyse the building stock from an
energy point of view, considering the thermo-physical and energetic characteristics of the buildings
and usually neglecting important variables that can affect the energy demand, such as the building
behaviour [7]. UBEMs investigate the energy issue related to the building stock following two main
approaches: the top-down approach in which the input are aggregated and the bottom-up approach in
which the input are in disaggregated form. The bottom-up models are the most suitable decision
support systems for local authorities thanks to the single building assessment of the energy
consumption, up to district level or at the global urban scale as a function of the energy end-use.
Meanwhile, the attention on the local microclimate and the outdoor comfort within the urban
environment is growing due to the changing climate and increase of heat stress in cities [8]. Outdoor
thermal comfort is often linked with the Urban Heat Island (UHI) phenomenon, although it depends on

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IOP Conf. Series: Materials Science and Engineering 609 (2019) 072018
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the combined effect of other environmental variables, i.e., wind speed, humidity and radiation, besides
air temperature [9]. The UHI effect on buildings consumption has been widely analysed [10]. Some
researches integrate the effect of other environmental parameters highlighting the necessity of further
improvements aimed at including the effect on outdoor thermal comfort [11], [12].
Scientific literature reports researches aimed at analysing the mutual interaction between buildings
performance and built environment [13]. A wide investigated issue is the effect of urban environment
on building energy behaviour [14] and its modelling [15]. To face this problem a common approach
provide the coupling of building energy and microclimate simulation tools, where typically the outputs
of the latter are used as boundary inputs of the former [16], [17].
The paper presents an integrated bottom-up approach based on energy and environmental analyses of
the built environment and allows a diffuse energy diagnosis of the building stock and it can support
the public administration to develop targeted energy policies. The paper describes the methodological
approach and the preliminary results of the research activity, aimed at characterizing the energy and
environmental performance of the urban fabric. The reliability of the system is tested on a real urban
district located in Bologna.
2. Methods
The methodology is designed according to a bottom-up approach aimed at analysing the urban area
from an energy and environmental point of view. The twofold analysis is carried out by considering
the available information provided by open-source database, mainly made available by the regional
territorial information system, needed to characterize both building stock and surrounding from a
geometrical, morphological and functional point of view. Table 1 summarizes the open-source
database consulted for the research and the data obtained. The use of these sources requires an initial
validation of the data in order to avoid abnormal values, which could affect subsequent analyses.
Table 1. Open-source database and data obtained for the research
Database
Data derived
Municipal base map
Geometrical features (building height,
volume, plan area), trees distribution
Town development plan
Prevailing intended use
Urban census
Year of construction
Weather station
Rural weather data
The energy performance of buildings are defined according to the bottom-up framework proposed in
[18], based on the definition of a reference buildings’ matrix. The environmental quality of the built
surrounding is evaluated through the Universal Thermal Climate Index (UTCI), a thermal index
defined within the cost action 730 and based on the multi-node ‘Fiala’ thermoregulation model [19].
2.1 Energy model
The aim of the energy model is the analysis of the energy behaviour of buildings at urban scale, in
order to detect energy-intensive areas and provide possible refurbishment scenarios. The model
provides the definition of a set of virtual buildings, grouped in a reference buildings matrix, obtained
by clustering the characteristics of the building stock. The thermo-physical and energy characteristics
of each reference building are derived from the TABULA building typology database for residential
buildings and the register of Energy Performance Certificate (EPC) for non-residential ones. The
energy performance of each virtual building is calculated in steady-state conditions.
Once the buildings’ matrix is defined, the data have to be assigned to each real building of the urban
area. For this purpose, a two-step approach is followed. The first phase consists of the definition of the
features of buildings at urban scale in compliance with the characteristics of the buildings matrix,
derived from the open-source database available at each Italian public administration. In this way each

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building can be characterized from the geometrical, functional point of view and year of construction,
as summarized in Table 1. The second phase consists of the detection of the characteristics of the
buildings through remote or in-field surveys in order to improve the quality of the information related
to each building. For example it is possible to identify a multi-intended use within the same building
fabric, possible refurbishment interventions and so on. At the end of this process, each building is
uniquely characterized and may be associated with the respective category of the buildings’ matrix.
2.2 Environmental model
The aim of the environmental model is the analysis of the distribution of UTCI considering a multi-
level approach. In particular, the environmental model consists of on a three-levels approach as a
function of the required degree of detail of the analysis. The first level considers only the effect of
direct sun/shade on people. The model does not account for spatial differences in both surface
temperature across the urban area and wind speed. It considers the effect of the solar radiation, which
is the largest driver of outdoor comfort during the daytime, and its effects on comfort and other rural
environmental parameters as provided by the nearest weather rural station. The use of specific
shapefiles related to trees distribution allows to analyse their shading effect and their influence on the
outdoor thermal comfort. The core function of the model representing the second level is the Urban
Weather Generator (UWG) [20], a python application that allows to morph rural weather data in order
to reflect the canopy conditions, by considering a range of simplified urban properties: building
geometry and intended use, urban materials, anthropogenic heat from traffic, vegetation coverage,
atmospheric heat transfer from urban boundary and canopy layers. Finally, the third level is based on a
series of components to connect Grasshopper3d, a parametric language, to ENVI-met, a software used
for urban microclimatic analysis. The resulting calculation model allows the following aspects to be
considered:
• solar radiation over the entire spectrum, from infrared to ultraviolet, respecting the shading,
reflection and emission of radiation from the system of buildings and vegetation;
• transpiration, evaporation and latent heat flows from vegetation into the air;
• Complete simulation of the physical factors of plants, such as the photosynthesis reaction and
its effects;
• surface temperature of the simulation area (buildings and ground);
• calculation of parameters such as the average radiant temperature and thermos-hygrometric
indicators of the users of the area under analysis;
• airflow and turbulence, wind data.
3. Case study
The energy model is tested on a district of Bologna, Italy. The district consists of 580 units in total,
where about 480 are buildings, with different intended use. About two-thirds of this set are single-use
buildings. In particular residential is the prevalent intended use (70%). followed by the services (12%),
commercial and educational (primary and secondary schools) buildings (respectively 7% and 5%).
Finally, cultural and recreational and healthcare buildings are present with less than 10 units and no
manufacturing buildings. Almost all multi-use buildings provides the coupling of residential and
commercial use. Buildings were mainly built in the period 1920÷1975 which implies a construction
type in “masonry” and “reinforced concrete”. Less of 5% of buildings were built in recent years.
4. Results
The application of the Energy model allows to quantify the energy behaviour of buildings within the
considered district. In particular Figure 1 shows these results in terms of global primary energy (EPgl),
expressed in kWh/m2y, defined by the sum of heating and DHW needs. The analysis of the numerical
results shows an high percentage of buildings (about two thirds) with a primary energy between 250
and 300 kWh/m2y. Less than 5% of buildings shows an EPgl value lower than 100 kWh/m2y. The
result is in compliance with the characteristics of the district, built towards the end of the 19th century.
Buildings are characterized with no or low-insulated envelopes and traditional heating systems
(centralized gas boilers).

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Figure 1. EPgl of the considered district.
The Environmental model is tested on a square of the considered district with dimension of about
170x110 m where the prevalent intended use of buildings is the residential one. The first level of the
environmental analysis allows to define the spatial distribution of UTCI considering the 3D district
context, the solar path and rural environmental parameters (Figure 2). The UTCI considers the rural
data about air temperature, relative humidity and wind speed. A specific component, described in [21],
allows to adjust the values of mean radiant temperature for the mesh of points (grid size 1 m, distance
from base surface 1 m) into which the surface of the urban context under analysis has been divided.
For the considered area and a specific period (a typical average values over one hour, 12 a.m. of
august 6th) a maximum and minimum UTCI values equal to 40.19 °C and 30.40 °C, respectively, are
calculated. The average UTCI is 34.95 °C and related standard deviation is equal to 3.58 °C.
Figure 2. Level 1: simple spatial UTCI
The second level is based on the UWG component that allows to consider a range of simplified urban
properties as input parameters (Table 2), thus allowing to define the canopy environmental conditions,
starting from rural data on the basis of a Resistance/Capacitance model.
Table 2. UWG input parameters
Traffic
Parameters
Vegetation Parameters Pavement
Parameters
City Building Typology
Max Heat: 10
W/m2
Albedo: 0.35
Albedo: 0.2
Buildings
height: 19 m
Midrise apartment
pre 1980s

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Weekday average
heat: 5.5 W/m2
heat latent fraction of
tree:
0.7 %
Thickness: 0.5m
Site coverage
ratio: 0.26
Floor area: 31,515
m2
Saturday average
heat: 4.3 W/m2
heat latent fraction of
grass:
0.5 %
Conductivity:
1 W/mK
Facade-to-site
ratio: 1.16
Footprint area:
5,233 m2
Sunday average
heat: 3.3 W/m
2
Heat capacity:
1600000 J/m
3
K
Tree coverage
ratio: 0.14
Facade area:
23,419 m
2
Grass coverage
ratio: 0.23
Glazing ratio: 45%
Considering the same square and the specific period, the spatial distribution of UTCI is similar if
compared with the previous case, however with lower maximum and minimum values equal to 38.87
°C and 28.96 °C, respectively. The average UTCI is 33.49°C and the related standard deviation is
equal to 3.67 °C.
Level 3 is based on the environmental data provided by the UWG, managed by the ENVI-met model.
Here below are presented the spatial distribution of UTCI for the considered area. The minimum value
is of 31.22 °C and the maximum 43.89 °C, whit an average of 39.72 °C and a standard deviation equal
to 3.72 °C (figure 3a). Figure 3b reports the difference in terms of spatial distribution of the UTCI
taking into account the rural environmental data, within the ENVI-met model.
Figure 3. Level 3: spatial UTCI considering ENVI-met: a) with UWG
environmental morphed data; b) difference considering rural environmental
data
5. Conclusions
The article describes a multi-objective methodology aimed at assessing the energy and environmental
performance of the built environment by analysing the energy behaviour of buildings and the thermal
condition of the surrounding. The methodology allows a deep characterization of the urban fabric with
several information about the state of the buildings and their energy performance. These data are
partially used as inputs by the environmental model to quantify the external thermal conditions
through different tools and related approaches. Future developments will provide an in-depth
investigation of the buildings characteristics based on on-site monitoring and surveys in order to guide
the decision-making process towards efficient and effective interventions. Furthermore, an
optimization procedure will be defined in order to optimize the outdoor thermal comfort analysis
considering all the available data at the district level and to facilitate the user in choosing the best
technical solutions for the building refurbishment.
Acknowledgments
The work described in the current paper is a partial result of the project RIGERS - Rigenerazione della
città: edifici e reti intelligenti (project number SCN00040), supported by the National Operational
Programme of Italy – Smart Cities and Communities.

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