A Sensor Network for Existing Residential Buildings Indoor
Environment Quality and Energy Consumption Assessment and
Monitoring: Lessons Learnt from a Field Experiment
Mathieu Bourdeau
1,2
, David Werner
1
, Philippe Basset
2
and Elyes Nefzaoui
2
1
CAMEO SAS 55, Rue de Châteaudun, 75009, Paris, France
2
Université Paris-Est, ESYCOM (FRE2028), CNAM, CNRS, ESIEE Paris, Université Paris-Est Marne-la-Vallée,
F-77454 Marne-la-Vallée, France
Keywords: Sensor Network, Energy Monitoring, Building Energy Efficiency, Energy Retrofit.
Abstract: Enhancing residential buildings energy efficiency has become a critical goal to take up current challenges of
human comfort, urbanization growth and the consequent energy consumption increase. In a context of
integrated smart infrastructures, sensor networks offer a relevant solution to support building energy
consumption monitoring, operation and prediction. The amount of accessible data with such networks also
opens new prospects to better consider key parameters such as human behaviour and to lead to more efficient
energy retrofit of existing buildings. However, sensor networks planning and implementation in general, and
in existing buildings in particular, is a particularly complex task facing many challenges and affecting the
performances of such a promising solution. In the present paper, we report on a field experiment of a sensor
network deployment involving more than 250 sensors in three collective residential buildings in Paris region
for the evaluation of a deep energy retrofit. More specifically, we describe the whole process of the sensor
network design and roll-out and highlight the main critical aspects in such complex process. We also provide
a feedback after several months of the sensor network operation and preliminary analysis of collected data.
Reported results path the way for an efficient and optimized design and deployment of sensor networks for
energy and indoor environment quality monitoring in existing buildings.
1 INTRODUCTION
Residential buildings are one of the major energy
consumers and greenhouse gas (GHG) emitters, with
38.1% of the final energy consumption and 36% of the
GHG emissions in Europe (ADEME, 2015). Efforts
have been made to reduce the impact of residential
buildings on energy consumption with various codes,
standards and thermal regulation mandatory
compliance for designs of new buildings (ASHRAE,
2013; HARMONIE, 2017). However, given the slow
turnover of the building stock in European countries,
the effect of these regulations is limited (INSEE,
2017). Hence, existing building retrofit turns into a
priority (ADEME, 2018). On the other hand, ambitious
massive smart sensor networks plans (European
Commission, 2019; European Parliament & European
Council, 2009; French Ministry of Ecological and
Sustainable Transition, 2016) have been launched in
recent years to enhance buildings energy efficiency.
On
the other hand, sensor networks have been
largely deployed in buildings for energy monitoring
and operation (Fan, Xiao, Li, & Wang, 2018) or
building energy consumption forecasting (Bourdeau,
Zhai, Nefzaoui, Guo, & Chatellier, 2019; Jain, Smith,
Culligan, & Taylor, 2014). Studies have used sensors
to highlight and characterize the link between energy
efficiency and inhabitants’ behaviour (Li & Lim, 2013;
Pisello & Asdrubali, 2014), identified as one of the
main source of energy performance gaps (de Wilde,
2014). Data collection solutions have also been
proposed to supervise building energy retrofits (Calì,
Osterhage, Streblow, & Müller, 2016; CSTB, 2016;
Jankovic, 2019). However, instrumentation solutions
deployment is a complex task, facing many challenges
and difficulties. Thus, it directly impacts on the quality
of the analyses and the efficiency of related energy
savings measures (Calì et al., 2016; Jankovic, 2019;
Pisello & Asdrubali, 2014).
In this context, we present lessons learnd from a
case study of a sensor network deployed in three
existing collective residential buildings in France. The
Bourdeau, M., Werner, D., Basset, P. and Nefzaoui, E.
A Sensor Network for Existing Residential Buildings Indoor Environment Quality and Energy Consumption Assessment and Monitoring: Lessons Learnt from a Field Experiment.
DOI: 10.5220/0008979401050112
In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pages 105-112
ISBN: 978-989-758-403-9; ISSN: 2184-4380
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
105
instrumentation takes part in a larger project that aims
to study the impact of deep energy retrofit measures on
the studied building energy consumption.
The present paper is organized as follows: we
present the project case study in Section 2, then, we
describe details of the instrumentation solution in
Section 3. A feedback on the main critical points for
sensor networks implementation in existing buildings
is proposed in Section 4. Finally, Section 5 presents
lessons learned from preliminary analysis of data
collected after several months of operation of this
experimental sensor network.
2 PRESENTATION OF THE CASE
STUDY
In the present research project, a group of three existing
social residential buildings – 63 apartments over a
4,600 m² living area – is considered for field
experimentation. Buildings were built in 1974 in Paris
(France) eastern suburb. Shared areas of the three
buildings and a 10-apartment sample are selected for a
three-year instrumentation period, starting in March
2019. The ten apartments comprise surfaces from 50 to
75 m² distributed in the three studied buildings, on
different floors and with different orientations.
Apartment case studies have one to two occupants of
various ages, professional occupation and living habits.
Case studies only had minor retrofit actions. They
are now planned to undergo deep energy retrofit
measures over an eighteen-month period in 2020-2021.
Retrofit measures will be implemented on occupied
site with tenants living in their apartments during the
whole retrofit period.
3 INSTRUMENTATION
METHODOLOGY
3.1 Instrumentation Plan
3.1.1 Purpose of the Instrumentation
The sensor network deployed aims to provide a multi-
scale and multi-targets wireless monitoring solution. It
focuses on data collection at both building- and
apartment-scale for energy consumption, outdoor and
indoor conditions, and inhabitants’ comfort and
behaviour. The installed sensor network needs to be as
non-intrusive as possible. This is essential to ensure
that the participants’ comfort and living habits remain
undisturbed to prevent any bias in the experiment.
The instrumentation solution described in the
present paper is the first step of a larger study. Data
collected through the sensor network should further
serve three consecutive purposes (Figure 1). First, data
analysis should highlight behavioural patterns and
energy drivers. Results should be used to obtain a
calibrated energy model of the buildings prior to
retrofit actions to predict their impact on buildings
energy behaviour and latter identify performance gaps
through a comparison with post-retrofit operation data.
Figure 1: Description of the main steps of the research project
starting with instrumentation planning and deployment.
3.1.2 Sensor Characteristics and Deployment
The sensors selection and deployment are performed in
two different phases, to set a total number of 259
sensors and connected objects (Table 1). The first
deployment phase covers shared areas and entire
building level. Although it involves only 10% of the
total number of sensors for 35% of the total cost. In this
phase, sensors provision, installation and data
communication are entirely managed by a hired
contractor. Four categories of measurements are
targeted:
overall and detailed electricity demand in shared
areas is monitored on the main electricity meters
and switchboards. A one-minute measurement
time-step is used to capture small electric events
such as for timed lighting and lifts usage;
building-scale thermal energy consumption is
obtained using ultrasound thermal energy meters
with a five-minute time-step (Figure 2);
occupancy is assessed using infrared presence
detection sensors positioned at the main entrance
door of the three buildings;
combined indoor temperature and humidity
sensors are positioned on three floors of each
building – ground floor, middle and last floor.
Since variations of indoor temperature and
humidity are not expected to abruptly change over
time, hourly measurement time-step is set;
SENSORNETS 2020 - 9th International Conference on Sensor Networks
106
a weather station is positioned on a nearby
university building to monitor local air
temperature, humidity, wind speed, wind
direction, rainfall and solar irradiation at 5-minute
time-step.
This first instrumentation step is now fully
operational, except for the weather station which is
currently being installed.
Figure 2: Installation example of a thermal energy meter for
heating energy demand monitoring (right).
The second part of the sensors focuses on the
characterization of apartments (aside from building-
scale monitoring of domestic hot water consumption
delayed to this second phase). This second deployment
phase should start in November 2019. Categories of
measurements are:
electric power demand from the main electric
meter and switchboard, and smart-plugs to
monitor all the main appliances of the
instrumented apartments (one-minute time-step);
hot water consumption assessed using contact-
temperature sensors on hot water pipes (one-
minute time-step) and completed with data from
already-installed remote reading volumetric water
meters;
heating energy consumption is deduced in a
similar way using contact-temperature sensors
installed on heaters (hourly time-step);
natural gas consumption, only used for cooking, is
monitored with sensors installed on apartments
gas meter (one-minute time-step);
indoor conditions and comfort should be captured
using contact-temperature sensors on exterior
walls of the apartments and a sensor combining
indoor temperature, humidity, luminosity, CO
2
and presence measurements (hourly time-step);
occupants’ behaviours should mainly be
characterized through occupation data and using
sensors for window opening-closing detection.
As for building-scale measurements, the choice of
time-steps is driven by the events sensors are targeting
and the purpose of data analyses. For electricity, hot
water and gas consumption, usages may only last a few
minutes. Therefore, the smaller time-step the better.
Regarding, heating energy consumption and indoor
environment, they are more likely to change on an
hourly basis. Finally, window-opening detection is
event-driven while occupation detection is linked to
indoor environment monitoring time-step.
3.1.3 Communication Network and Online
Data Collection
Data communication for the present sensor network is
entirely wireless (Figure 3). Several, communication
protocols are available such as Modbus (The Modbus
Organization, 2019), Sigfox (Sigfox, 2019) or
LoRaWan. The latter is currently the most common for
applications such as the one presented in this paper
(Augustin et al., 2016) and most sensors available on
the French market use LoRa technology. Indeed, LoRa
is fit for IoT projects and wireless sensor networks
deployed in smart buildings and smart cities contexts
(Centenaro, Vangelista, Zanella, & Zorzi, 2016;
Pasolini et al., 2018). It is relatively easy to implement,
it provides information on the sensor status and it
allows long-range data transmission even in areas with
difficult access. LoRa technology is also energy-
effective as it relies on radio waves with low
communication rate and then low energy consumption.
Several implementation strategies have been selected
for the two presented instrumentation phases. The first
phase uses an operated LoRa network with two
gateways installed onsite. Onsite LoRa communication
tests led to the conclusion that a single gateway would
be sufficient. However, a second one is installed in case
of reliability. Only the electricity consumption-
dedicated sensors use a GPRS network because of their
acquisition time-step and the limitations of operated
LoRa network in terms of bandwidth usage (Electronic
Communications Committee, 2019).
The second phase is processed differently and
separated into three sub-phases. As for shared areas,
energy monitoring is entirely managed by a contractor.
Other sensors are provided and configured by a
different service company and installed onsite by the
research group in charge of the project.
A Sensor Network for Existing Residential Buildings Indoor Environment Quality and Energy Consumption Assessment and Monitoring:
Lessons Learnt from a Field Experiment
107
Table 1: Characteristics summary of the deployed sensors.
Sensor
Number at
building scale
(per building)
Number
per
apartment
Total
number
Acquisition
time-step
Communication
protocol
Indoor temperature &
humidity
3 / 9 Hourly Operated LoRa
Presence 1 / 3 Hourly Operated LoRa
Combined indoor temperature,
humidity, luminosity, presence
& CO
2
level
/ 1 10 Hourly Private LoRa
Exterior walls surface
temperatures
/ 2-5 32 Hourly Private LoRa
Window opening detection / 3-5 38 Event-driven Private LoRa
Gas consumption / 0-1 4 One-minute Private LoRa
Electricity demand – main
electric meter
2-3 1 17 One-minute GPRS
Electricity demand –
switchboard
0-1 1 11 One-minute GPRS
Electricity demand –
smartplugs
/ 5-9 67 One-minute Private LoRa
Heating energy demand –
thermal energy meter & pulse
sensor
1 + 1 / 3 + 3 Five-minute Operated LoRa
Heating energy demand –
radiators
/ 3-5 36 Hourly Private LoRa
Domestic hot water energy
demand – thermal energy
meter & pulse sensor
1 + 1 / 3 + 3 One-minute Private LoRa
Domestic hot water energy
demand – apartments
/ 2 20 One-minute Private LoRa
Finally, a ten-sensor sample (those combining
measurement of indoor temperature, humidity,
luminosity, presence and CO
2
level for apartments) is
entirely set up and installed by the research group.
The used LoRa network is configured as private
network to bypass bandwidth usage constraints
related to data acquisition time-step.
For all sensors, collected data are retrieved on
three FTP servers: a first server used by the contractor
for managing part of the instrumentation solution, a
second sensor for equipment installed by the research
group and a third server dedicated to data retrieved
from the weather station. The final format provides
collected raw data with one csv file for each day and
for each sensor. Information include the sensor
identification and location, the type of measurements,
the timestamp, the measured values and the units.
Figure 3: Simplified diagram of the wireless sensor network.
3.2 Instrumentation Management
Process
Instrumentation overall management has a crucial
impact on the success of the instrumentation and on the
whole project. In the present study, it can be divided
into six different stages over an estimated timespan of
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one year and four months (started in October 2018 and
expected to end in January 2020).
First is the accurate definition of the
instrumentation specifications to meet the needs of the
project. In parallel, a review of service providers
offering adapted solutions is performed and apartments
are recruited to participate to the instrumentation study.
Once specifications are organized in a clear
framework, they are submitted to identified
contractors. Several rounds of discussions allow
modifications on the specifications. It aims to adapt to
the reality of the market and for service providers to
refine their offer to meet the needs of the project to the
best of their capabilities. It also helps select the most
relevant contractors. In the present case thirty-three
service providers were contacted and two contractors
were finally selected.
Once a contract is signed, sensors are ordered,
delivered and installed under the supervision of the
research group. Finally, when the sensor network is
functional, data quality is checked. It ensures that all
sensors are properly measuring and transmitting data,
that there are no missing information or other issues
and that the proper data format is displayed for future
applications.
4 IDENTIFTICATION OF
CRITICAL POINTS FOR
SENSOR NETWORK
IMPLEMENTATION
The planning and deployment of the proposed
instrumentation solution has highlighted several
critical aspects in sensor network implementation.
These critical points arose from various issues
encountered during the instrumentation process. These
can be grouped into four categories with i) onsite
installation conditions and environment, ii)
characteristics and purpose of instrumentation, iii)
service provider and iv) project tracking and
management.
4.1 Onsite Installation Conditions and
Environment
Characteristics of the experimentation site have a
significant impact on the feasibility of a monitoring
project. As a first step, it is crucial to identify the
equipment and installations. Equipment conditions are
important, as for security reasons some may not be
monitored. Indeed, over the selected ten-apartment
sample, seven electrical switchboards could not be
equipped because of their obsolescence and should be
replaced for the experimentation. Sensor connectivity
must also be considered for sensors placement and to
choose the relevant number of gateways necessary
since metal stairs and doors disrupt radio waves and
then data transmission. Finally, the installation
environment should be documented. For instance,
several sensors do not use batteries but need a main
power supply – thermal energy meters for instance. In
the buildings, such electrical installations were not
adapted or even not existing where they were needed.
Thus, additional costly actions were necessary to
prepare the installation site.
The present instrumentation solution is also
specific because all buildings remain occupied during
the sensor network deployment and the whole
experimentation. Then, the sensor network must be as
non-intrusive as possible. However, non-intrusive
sensors imply higher costs due to specific technologies.
For instance, it applies with ultrasound thermal energy
meters and with all electricity-related meters that need
to be small, easy movable and without heavy
installation work on the existing equipment.
Also, as the project mostly relies on collected data
from apartments and occupants’ behavior, it is
necessary to recruit participants. Then, recruitment is a
crucial step for the success of the research project, but
is particularly time-consuming, requiring several
rounds of presentations and visits (general
presentation, door-to-door visits, phone calls and
individual meetings). In the project, up to three months
were necessary to recruit ten households. Moreover, it
is crucial that inhabitants find clear benefits in their
participation. In this case it was proposed for
inhabitants to have access to all collected personal data
and summary analysis results for their households.
Finally, as most collected data are personal data, a
specific attention should be given to related regulations
and mandatory administrative actions (French Ministry
of Economy, 2019).
4.2 Characteristics and Purpose of the
Instrumentation Solution
The introduced sensor network serves for a research
project with specific needs. Therefore, it differs from
usual commercial monitoring studies because it aims
to investigate situations and details that would usually
not be considered. More specifically, there are a wide
range of acquisition time-steps (from one minute to one
hour) and a large variety of sensors and measurements
as presented in Table 1.
Consequently, such a complex instrumentation
solution faces several challenges. For instance, sensors
A Sensor Network for Existing Residential Buildings Indoor Environment Quality and Energy Consumption Assessment and Monitoring:
Lessons Learnt from a Field Experiment
109
are designed and manufactured for standardized usage
conditions depending on the environment of
installation and usually with hourly acquisition time-
step. Their internal memory stores up to one day of data
(24 measurements) communicated once a day. Then
using sub-hourly acquisition time-steps requires more
frequent data communication which is energy
intensive and significantly reduces the expected battery
lifetime. However, sensors such as for gas
consumption metering (Zone-atex.fr, 2019) or used
outdoor or in damp and dusty environments are airtight
and cannot be opened once installed. This prevents the
battery to be replaced and induces a costly replacement
of the whole sensor instead.
Sensor technologies and measurement
characteristics (especially the acquisition time-step)
also have an impact on the design of the sensor
network. There is first the choice between the different
existing communication protocols with different
characteristics, advantages and constraints. LoRa is the
prominent solution on the market and was selected in
the present case study but it presents drawbacks. For
instance, operated LoRa networks restrict bandwidth
usage. Then having a one-minute time-step on large
amounts of sensors is hardly possible. Private LoRa
networks offer a bypass solution since they are locally
set networks dedicated to one specific sensor network.
However, they are more complicated to implement.
Other solutions such as GPRS used for some specific
sensors also raise different issues related to wireless
monitoring technologies and electro-sensitivity.
4.3 Service Providers and Contractors
Through this study, service providers have been hired
to provide an instrumentation solution adapted to the
goals of the research project. On the French IoT
market, there are many different contractors. However,
they do not all provide the same delivery. Most
commercial proposals focus on sensor provision but do
not include set up and installation. Therefore, such
contractors must partner with other companies to
match the needs of a project. This often leads to many
communication issues and delays. Furthermore, when
a contract is established, many terms must be carefully
checked to ensure a smooth project process, and more
particularly: i) responsibilities and guarantees about
the sensors/network and about the potential provision
and installation delays, ii) maintenance details and
conditions when included, and iii) what is done by the
contractor with collected data during and after the
contract validity.
It is also necessary to ensure the service quality and
expertise of contractors. Indeed, there is a significant
difference between common “plug and play” sensors –
such as for temperature or humidity sensors – and
energy metering. The latter requires very specific
knowledge on the technologies, installation procedures
and data acquisition checking. Many contractors do not
have such equipment and need to partner with other
manufacturers. This lack of expertise often leads to
many future issues with sensor calibration or
equipment failures.
Finally, the budget management requires a detailed
investigation. Indeed, there can be several unexpected
expenditure items that can significantly increase the
total instrumentation budget: include miscellaneous
accessories, connectivity subscriptions, details of all-
inclusive maintenance contracts, installation and setup,
or site visits. A budget summary from the ten complete
commercial proposals received is presented in Figure
4.
Figure 4: Budget summary for the presented sensor network.
4.4 Project Management
The overall project management, comprising
supervision on the three previous subsections, is the
key to a smooth sensor network deployment and
optimal valorisation. Regarding the instrumentation, a
detailed and precise tracking is mandatory to identify
every sensor with sensor type, measurements,
communication protocol, installation date and location.
Adapted and controlled communication is essential
within the research group to prevent any loss of
information and miscommunication. It also must be
ensured with other actors and particularly with
building occupants. Indeed, it is recommended to avoid
any over-soliciting with inhabitants for obvious
privacy reasons. Finally, as the present research project
is a multi-disciplinary study combining several
research teams, an optimal coordination is needed.
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110
5 PRELIMINARY DATA
ANALYSIS FROM THE FIRST
INSTRUMENTATION PHASE
The first instrumentation phase focusing on building
common areas is fully operational. Thus, a statistical
data analysis is performed on an observation window
from 2019/02/19 to 2019/10/06 to highlight issues in
collected data. Twenty sensors are considered
including data for temperature & humidity (9 sensors),
passage counting (3 sensors), thermal energy (3
meters) and electric power demand monitoring (5
sensors).
The main highlighted issue relates to missing
datapoints. Over the given observation period, 92.7%
of the expected datapoints are collected. Figure 5
compares the evolution of the expected number of
measurements (blue curve) and of the effective
collection results (orange curve). The percentage of
acquired data over time is presented on the green curve.
Figure 5: Evolution of collected datapoints (orange)
compared to expected collected datapoints (blue) and
percentage of acquired data (green) from 2019/02/19 to
2019/10/06.
The major gap is due to a 17-day-long sensor
malfunction in May 2019 for the electrical switchboard
monitoring. Electricity demand data collection is
therefore the category with the least collected
datapoints (on Figure 6: 92.0% of the optimal target),
followed by thermal energy monitoring (94% of data
collected). Indoor environment and passage counting
account for more than 99% of collected data. However,
large amounts of missing energy datapoints should also
be transposed to time-scale analysis. Indeed, electric
sensors provide one-minute time-step information.
Hence, over a 230-day observation period, it would be
equivalent to a loss of less than 19 days of data
concentrated on a specific period for one specific
sensor.
Other observed outliers are abnormal and
additional measurements. Abnormal values are only
found for energy demand monitoring, with overly high
measurements or zero-values. They respectively
represent 0.06% and 6.8% of total. Zero-values are
almost exclusively related to electrical switchboard
monitoring (99.9% of abnormal measures).
Figure 6: Comparison of collected datapoints (orange)
compared to expected collected datapoints (blue) for the
different monitoring categories.
6 CONCLUSIONS AND FUTURE
WORK
This paper presents the planning, deployment and
supervision of a sensor network for existing residential
buildings monitoring. The proposed solution is a multi-
scale sensor network covering thermal and electrical
energy consumption, indoor environment and comfort,
inhabitants’ behaviour and weather conditions, at
various timescales.
The instrumentation solution has been entirely
planned and divided into two implementation phases.
The first phase focuses on the monitoring of building
shared areas and is operational. The second phase
considers a similar approach on a ten-apartment
sample and will be deployed starting from November
2019. Details of both phases are presented with a
description of sensor characteristics, communication
networks and overall project management.
A feedback on the first phase of the sensor network
implementation is provided. It shows the obstacles and
challenges when implementing large sensor networks
with a wide diversity of measurements in existing old
buildings. It highlights several critical points to be
considered for similar future applications. These
critical aspects are grouped in four categories with
onsite installation conditions and environment,
characteristics and purpose of the instrumentation
solution, service provider and contractors and project
management. A preliminary statistical analysis of
A Sensor Network for Existing Residential Buildings Indoor Environment Quality and Energy Consumption Assessment and Monitoring:
Lessons Learnt from a Field Experiment
111
collected data is also provided. It highlights the main
encountered issues in data collection.
In future works, data analysis will be extended to
measurements to the second instrumentation phase
data. Moreover, in order to ensure long-term
measurement accuracy and to prevent future avoidable
data loss and highlighted data collection issues, a long-
term calibration study will be conducted. Finally,
collected data will be integrated in building energy
models to characterize the buildings behaviours and
investigate potential issues in building energy
management.
ACKOWLEDGMENT
This work was supported by the I-SITE FUTURE
Initiative (reference ANR-16-IDEX-0003) in the frame
of the project ANDRE.
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