Stay Informed
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People in
industrialized societies spend most of their lives indoors. In the past decades,
energy saving measures have led to the construction of airtight buildings. This
can negatively impact the indoor air quality by allowing a build-up of air
contaminants within a building section if sufficient ventilation is not
provided. In residential buildings in Germany the dominant mode of ventilation
is natural ventilation through hand operated windows. Healthy indoor air
depends on the rate of delivering fresh air to the environment and also on the
indoor production rate of the contaminants. Especially in heating season an
excessive ventilation time or an incorrect ventilation type can result in
higher heating energy consumptions, whereas a less-than-necessary ventilation
time leads to an accumulation of contaminants in a room and therefore causes
dissatisfaction of the occupants.
Poor indoor
air quality (IAQ) in residential buildings can also have direct economic
drawbacks. An increasing number of employees working from their homes signify
the economic importance of the IAQ in residential buildings. Better IAQ results
in more productive and happier occupants. While it is difficult to exactly
quantify these benefits, there is continuing evidence of higher productivity in
areas with better IAQ.
The
sensitivity towards the actions controlling air quality is very diverse. About
59% of Europeans seem to lack information about the air quality issues in their
country [1]. The survey was conducted mainly to assess the topic of general air
quality, but it gives an impression of the number of people who may also be
uninformed about the topic of IAQ in their homes.
Many
studies have shown the effectiveness of feedback systems in persuading
occupants to have a more IAQ-aware and energy-efficient ventilation in their
buildings (see e.g. [2] and [3]). The advantages of feedback systems are
twofold. They represent a low-cost method to promote efficient behaviour and
therefore decrease energy consumption, and behavioural persuasions are less
likely to produce the undesired rebound effect.
In most
cases feedback is carried out through information campaigns and printed media,
which address the general consumer and aim at listing and describing all
relevant cases of efficient behaviour. With the advent of modern low-cost
sensors and communication, as well as data processing technology, there is an
opportunity for a dynamic and active feedback system. The purpose of this study
is to provide the proof of concept for a personal feedback system using
low-cost sensors with a controller unit to increase awareness of the state of
the IAQ and to promote better indoor air quality, and efficient ventilation
behaviour in naturally ventilated rooms among occupants in residential
buildings.
The ambient
status is gathered by a set of sensors that relay data to a single-board
computer (Raspberry Pi with a USB power source) where the processing and
storing of the data are carried out using an open source robust data collection
and automation software. The data is stored locally on a memory disk and on an
external hard drive for further analysis. The concept of this system does not
introduce actuators like in classical smart home systems therefore eliminating
the related investment, maintenance and operational costs. The system acts as a
suggestion platform and actively provides feedback to the occupants. Figure 1
shows the schematic of the design principle.
Figure 1. Schematic of the design principle.
The
recorded data includes temperature, relative humidity, CO2 concentration, illuminance and occupant presence. Also, the duration of
certain occurrences (such as the duration of a temperature drop, or open window
status or the time it took for the occupant to react on the recommendation) can
be recorded. These data can generally be used to give feedback in the areas of
lighting, window shades’ status, heating and ventilation. The setup is compact
and can be placed near the sitting area in a living room or on the work desk in
a home office. In the current stage, the feedback rules are kept simple and
straight-forward. The recommendations are given using messages on displays,
optional short beeps and coloured LEDs. Figure 2 demonstrates the test values of the
CO2 concentration, the type of the display used
and the possibility of input parameters through the software used.
b. | |
c. |
Figure 2. a) Test values of the CO2 concentration. b) Possibility of input parameters. c) Prototype display
used for message communication (© RaspberryPi)
Feedback
possibilities that can be achieved using these sets of sensory data are very
diverse and still in development. In the following, an example of active
IAQ-feedback on efficientnatural ventilation behaviour is described.
In this
study, the basis of identifying the quality of indoor air is the CO2 concentration. There are two operational modes for the IAQ-Feedback:
“continues monitoring” and “feedback on demand”. In both modes, an average is
taken every minute from continuous measurements. The value is used in the following
feedback algorithm of the “continues monitoring” mode and also stored for further analysis.
Whenever the value raises above 1000 ppm the feedback device performs
a short beep and using
its displays, gives a recommendation on the necessity of acquiring fresh air
through fully opening the windows and a hint on closing the radiator valves. If
the concentration value continues to increase,
another beep at 1500 ppm will alert the user(s). At the concentration of 2000 ppm
a red light emitting diode (LED) points out the importance of conducting
ventilation with blinking and subsequent on-status. If the system
assesses a drop in the CO2 values, the red LED will go off.
A beep is provided when a concentration of 700 ppm is
reached as a sign of sufficient air quality and the display message will
vanish. In this design, the limits of the CO2 concentration follow the Pettenkofer value and the
recommendation of the German environmental agency (UBA) for a traffic lights
concept for schools. These limits can be adjusted as input parameters.
In “feedback-on-demand” mode the feedback on the CO2-based quality of the room air is only given when the user asks
for feedback by pressing a button. In the background, however, the concentration
measurements are carried out continuously. If the feedback-on-demand button was
not used on one day or if the CO2 concentration values were above
1000 ppm, a short report will mentionthe number of
minutes with a CO2 concentration
above 1000 ppm on the next day.
Feedback systems on CO2 can also represent
a low-cost solution to increase the energy efficiecny in resdential buildings, since a fundamental
factor in energy-efficiency of heating a household is the behaviour of the
occupants, particularly how they ventilate rooms. This approach belongs to the
category of behavioural interventions. A recent study also showed that a ‘CO2-meter’ can change user’s behaviour and improve
indoor air-quality [4].
Furthermore,
surveys have shown that a determining portion of the society in Germany is
still feeling ‘uneasy’ with the increasing amount of automated processes in
their homes [5]. Some mention that they would like to have the feeling of
control over the events more often. The product resulting from the prototype in
this study can represent a solution for this part of the society to still
benefit from the advantages of the modern technology to receive environmental feedback, yet keep the decision-making power for themselves.
It is important to remember that the feedback system does not fully
replace the decision-making or judgment ability of the occupant. It suggests that the occupant has
the opportunity to improve the indoor air quality by opening the windows if
possible. If the outdoor air is of particularly
low quality the user does not have this option. In these cases (for example in
polluted cities) the feedback system of next generation will use an outdoor
unit and focus on delivering feedback on the time of the day where outdoor air
pollution (defined according to DIN EN 16798 or WHO – World Health
Organization - Air quality guidelines including
PM10 and PM2,5) is lower than the indoor air pollution.
Figure 3. Prototype of the sensor box (© D.Boehnke)
The described solution is at the design stage. The CO2 sensor is integrated on a sensor box (seeFigure 3) which includes further sensors for future applications. In the next generations,
·
the window state will be available directly using wireless
magnet sensors.
·
the temperature data will be integrated to dynamically
determine the feedback time for closing the windows.
·
the concepts of the design of human-building-interaction are
applied to promote the interaction between the occupant and the room and raise
the probablity of action on feedbacks.
The
significance of integrating occupants into the operation of the building is
increasingly acknowledged by the building research community. The author’s
vision is to combine the effective know-how on the positive influence of
eco-feedbacks with the new possibilities of the modern information and
communication technology (ICT) to develop a system for real-time human-centred
interactions between rooms and humans with a focus on improving indoor
environmental quality and energy efficiency. In the concept of human-room
interaction, the rooms in residential buildings will not be considered only as places
of rest and gathering but as active dynamic entities interacting with the
occupants resulting in increased satisfaction and wellbeing of the citizens.
[1] Attitudes of Europeans towards Air quality,
Report by TNS Political & Social at the request of the European Commission,
January 2013.
[2]
Abrahamse W.,
Wokje A., Steg L., Vlek C.,
Rothengatter T., A review of intervention studies
aimed at household energy conservation. J. of Env.
Psychology 25, 3 (2005), 273–291.
[3]
Kester C., James W., Gerber S., Saving
Energy by Behavioral Changes, Kansas State University
Campus plan, NRES Capstone Course, 2015.
[4]
Energy-efficiency impacts of an air
quality feedback device in residential buildings, Energy and Buildings, 116
(2016), 151-163 DOI: 10.1016/j.enbuild.2015.11.067
[5] Grieger & Cie
Market study, Smart Home Markt Deutschland, 2016.
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