Long-term monitoring data from a naturally ventilated office building

Abstract

Data was collected in the field, from an office building located in Frankfurt, Germany, over the period of 4 years. The building was designed as a low-energy building and featured natural ventilation for individual control of air quality as well as buoyancy-driven night ventilation in combination with a central atrium as a passive cooling strategy. The monitored data include in total 116 data points related to outdoor and indoor environmental data, energy related data, and data related to occupancy and occupant behaviour. Data points representing a state were logged with the real timestamp of the event taking place, all other data points were recorded in 10 minute intervals. Data were collected in 17 cell offices with a size of ~20 m², facing either east or west). Each office has one fixed and two operable windows, internal top light windows between office and corridor (to allow for night ventilation into the atrium) and sun protection elements (operated both manually and automatically). Each office is occupied by one or two persons.

Full text

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Long-term monitoring data from a
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
*r & Andreas Wagner
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
e introduction of the European “Energy Performance of Buildings Directive” in 2001 gave a strong incentive
to reduce excessive energy consumption through a holistic approach in terms of building design and integrated
energy concepts. Within this context, the program SolarBau, introduced by the German Federal Ministry of
Economic Aairs, funded ambitious demonstration projects within the non-residential building sector setting
benchmarks in terms of low primary energy consumption. Buildings in Germany are benchmarked through their
primary energy demand for heating, cooling, ventilation, lighting and domestic hot water (DHW). e moni-
tored building described here had a projected value of 107 kWh/m²a.
Within this program, a strong focus was set on various passive cooling strategies in combination with a higher
insulation standard than required by German regulations in the year of construction (see Table1 for details).
Daylight factors above standard at the workspaces were achieved by proper window design and light directing
devices (venetian blinds with dierent blind positions and ceiling panels above the desks). At the same time,
occupants’ interactions with windows and blinds – essential aspects in the context of passive cooling concepts –
was addressed1–3 as well as their thermal comfort under these conditions. A two-year monitoring aer commis-
sioning of the building was compulsory for a proof of concept for all funded buildings.
Inuencing factors on the occupants’ behaviour with regard to the operation of windows and blinds are,
among others, the indoor and outdoor environmental conditions such as temperatures, relative humidity levels,
air quality levels, and lighting levels4,5. Due to their daily and seasonal variation, long-term monitoring data, i.e.
at least a full year, is essential to capture their inuence on occupants’ behavioural patterns.
e monitored building is located in Frankfurt am Main, Germany. Key characteristics of the building are
presented in Table1. An important design feature to enhance natural night ventilation is a large atrium with
an extended “chimney” around which the oces are located. is enables a buoyancy-driven airow from the
windows through the oces themselves, into the trac zones, and then up into the chimney where the air leaves
the building. e airows through the oces are levelled out by the opening angle of the top lights, located above
the manually operable windows. Directly exposed concrete ceilings in the oces enable the activation of thermal
mass as an essential part of passive cooling by night ventilation. Furthermore, the atrium increases the usage of
natural lighting for the interior trac zones.
Only the meeting rooms, the oces to the south behind the double-skin facade, oces with suspended ceil-
ings and a number of special purpose areas are actively cooled, but are not part of this dataset.
e occupant is able to open the windows manually. For operating the top–light windows (see Fig.1) occu-
pants have to use the control panel which is located beside the door (opposite to the façade). rough this panel,
Building Science Group, Karlsruhe Institute of Technology, Englerstr. 7, 76131, Karlsruhe, Germany. *email: marcel.
schweiker@kit.edu


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occupants can also control the exterior Venetian blinds and the articial lighting of the oce. Outdoor noise due
to trac was only present in the area of the south facing rooms, which are not part of this dataset. No other noise
sources are known. Outdoor air quality was high, given outdoor CO2-levels (included in data) being mean 465
ppm ± 52 (standard deviation).
e dataset published consists of long-term data from January 2005 to December 2008, i.e. starting 2 years
after the construction, when initial problems with controls where already solved. The data published has a
10-minute interval for continuous data and the event data with individual time stamps and consists of data from
17 oces.
e data has been analysed and used by several authors. A rst analysis of the indoor environmental con-
ditions and energy revealed that indoor air quality levels were high, that the primary energy consumption was
at a low level as predicted, and that the monitoring was a useful measure towards an optimized operation6,7.
Schakib-Ekbatan et al. questioned the t between occupant behaviour and the building concept and found several
occurrences of occupants’ window opening behaviour contradicting the natural ventilation concept8. An addi-
tional analysis of the data applied a data mining framework for identifying occupancy patterns and found four
archetypal working proles9.

 In order to collect long-term data automatically and frequently, all sensors were con-
nected to the building management system (BMS) of the building. Data were gathered in 10-minute intervals or
as event data. Data was stored for one day locally and send at night as csv-les to the server of the researcher. Data
was stored in a MySQL database.
A weather station was located on the top of the building at 2 m above the roof, i.e. around 30 m above street
level. e weather station is providing data regarding the outdoor conditions for all oces, such as temperature or
wind speed. However, the microclimate on the façades can dier, e.g. depending on the intensity and direction of
wind. e precipitation meter was not heated. However, snowfall is seldom. ere are no direct obstacles close-by
aecting the wind speed or direction. Still, wind speed and direction might have been aected by the buildings
in the neighbourhood.
All oces included in this data base have a size of ~20 m2 and are facing east or west (see also Table2). Each
oce has one xed and two operable windows with top light windows above, internal top light windows between
oce and corridor (to allow for night ventilation into the atrium) and external sun protection elements (operated
both manually and automatically). One or two persons occupy each oce.
Presence of occupants was measured by an infrared sensor located in the middle of the ceiling panel, which is
suspended from the ceiling above the work places.
Type of building Multi-storey oce building
Dimension 17,402 m2 (8,585 m2 heat ed)
No. of Employee ~350 employees
Location Frankfurt, Germany
ermal characteristics High energy standard of building envelope
Walls: U-values 0.24 to 0.5 W/m2K)
Windows: U-values 1.5 W/m2K, solar transmittance <40%, light transmittance 70%
Structural system Reinforced concrete construction
Type of observed spaces Oce rooms
Year of construction 2002
No. of oors 2-level underground car park +4 oce oors + 1 oor apartments on top
Window dimensions Windows:
Top lights
Windows, orientation Mostly E and W
Window opening
All windows open inwards. No obstacles prevented window opening except those potentially added by
occupants (e.g. plants or papers placed in front of window)
Windows: Manual opening through window handle by occupants only, windows had hinges on one side and
could be fully opened (rotated) to any degree up to 90° opening angle Top lights: automatic control + manual
opening through switch next to oce door by occupants, windows had hinges at the bottom and opened on top;
any degree up to ° was possible at manual control; at automatic control for night-time ventilation the angle was
predened in order to balance pressure dierence between oors and achieve almost the same volume ow for
each oor and oce.
Window control options Automated 10 minutes ush ventilation before working hours through top lights in the façade and between
oce and corridor
Aerwards: Top lights: Tilt (automatic + occupant driven mode), Windows: Tilt-and turn (occupant driven)
Shading devices External sun protection (automatic + occupant driven mode) with dierent angle of blinds in the upper part to
provide daylight guidance. Sun protection consists of light metal Venetian blinds with a slats width of 80 mm
and a reectance of 60%..
Predicted annual primary
energy consumption 107 kWh/m²
Monitored annual primary
energy consumption 100 kWh/m² in third year of monitoring
Tab le 1. Building characteristics.
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Air temperature, relative humidity and CO2-level were measured inside each oce close to the oce door at
1.1 m height through a device attached to the walls separating the oces from the corridor.
Occupants can change the status of top-light windows, blinds, and lighting through a set of buttons close
to the oce door. Windows can be opened directly at the façade. Status of windows was measured through
reed-contacts connected to the buildings’ BMS system. Position of the blinds was measured based on blinds’
motor run time.
e data points available in the database are presented in Table3. ese data can be grouped into outdoor
environmental data, indoor environmental data, energy related data, and data related to occupancy and occupant
behaviour.

All data records listed in this section are available from the project pages10 on Open Science Framework (OSF)
and can be downloaded without an OSF account. We licensed the data under a CC0 1.0 Universal license.
 File format: comma separated values le (.csv). Data is available as one le for each sensor
including date and time column. All date formats are in the format day, month, year, i.e. dd.mm.yyyy. Devices in
use are recorded with 1 or 100, those not in use with 0. is translates to open windows being in the data recorded
as 1 and completely closed blinds with values 100.
Fig. 1 Window positions and dimensions of one oce.
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 File format: comma separated values le (.csv).

Incoming datasets were analysed according to their completeness and validity. An error message was sent to the
researchers in case these checks revealed problems. ese analyses mainly targeted for checking availability of
data and to lter implausible or missing values. Missing values in air temperature, relative humidity, and CO2
were marked by a value of “0” and ltered automatically. Implausible values, e.g. indoor air temperatures above
35 °C, were agged by the monitoring soware and manually inspected using the visualization tools of the mon-
itoring soware. e monitoring soware used was MoniSo11.
e air temperature sensors were checked and calibrated during commissioning by the facility management
and later comparison through a high-quality comfort meter equipment in sample rooms showed good con-
formity. All other sensors had been calibrated by the manufacturer, but could not be calibrated again during
operation.
Room
ID Room air
temperature Occupancy Window
control Top window
control Sun
protection Electricity
demand lighting Electricity
demand plugs CO2− Concen-
tration
East
E01 E01Tair E01Occ E01W E01WT E01SP
E02 E02Tair E02Occ E02W E02WT E02SP
E03 E03Tair E03Occ E03W E03WT E03SP
E04 E04Tair E04Occ E04W E04WT E04SP E04CO2
E05 E05Tair E05Occ E05W E05WT E05SP
E06 E06Tair E06Occ E06W E06WT E06SP E06ElL E06ElP
E07 E07Tair E07Occ E07W E07WT E07SP E07ElL E07ElP E07CO2
E08 E08Tair E08Occ E08W E08WT E08SP
E09 E09Tair E09Occ E09W E09WT E09SP
E10 E10Tair E10Occ E10W E10WT E10SP
E11 E11Tair E11Occ E11W E11WT E11SP
Wes t
W01 W01Tair W01Occ W01W W01WT W01SP W01ElL W01ElP
W02 W02Tair W02Occ W02W W02WT W02SP
W03 W03Tair W03Occ W03W W03WT W03SP
W04 W04Tair W04Occ W04W W04WT W04SP
W05 W05Tair W05Occ W05W W05WTaW05SP W05ELP W05CO2
W06 W06Tair W06Occ W06W W06WT W06SP
Tab le 2. Orientation and variables available for each oce. aNote that no data le is provided for this sensor,
because no event was recorded over the monitoring period.
Categories of data Subcategories of
measured data Va riable Interval
Sensor
Range Accuracy
Inhabitants
Other Presence (all rooms) Event — —
Other Window state (open/closed) Event — —
Other Top-light window state (open/closed) Event
Other State of sun protection (open/closed) Event — —
Indoor conditions
Hygro-thermal Air temperature 10 minutes 0–40 °C ±0.1 K
Hygro-thermal Relative humidity (all rooms) 10 minutes 0–100% ±1%
Indoor Air Quality CO2-level (3 rooms) 10 minutes 300–3500 ppm ±3%
External conditions
Hygro-thermal Air temperature 10 minutes −40–+ 45 °C ±0.1 K
Hygro-thermal Relative humidity 10 minutes 0–100% ±2%
Visual Illuminance (4 orientations + horizontal) 10 minutes 0–100,000 lx ±5%
Solar radiation Horizontal solar radiation 10 minutes 0–1300 W/m² ±2.5%
Other Precipitation (Amount and event) 10 minutes — —
Other Wind speed and direction 10 minutes 0–360° 0–20 m/s —
Energy
Heating/cooling Overall heat quantity pellet boiler and gas boiler 10 minutes ±1%
Lighting Lighting energy (3 meters for 5 rooms) 10 minutes ±0.5%
Equipment Plug loads separated by IT and other (3 rooms) 10 minutes ±0.5%
Other Total electricity use of building 10 minutes ±0.5%
Tab le 3. Variables, their categories and subcategories according to the ontology for building monitoring13, and
intervals.
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
By the general and open csv format the researcher is free to use whatever soware s/he nds suitable for analysing
or visualising the data. For comfort analysis the R-package comf is recommended12.
For further analyses, it needs to be considered, that top window and blind states were either changed through
the BMS or manually by occupants. e algorithm of automatic controls of top windows and blinds is unknown.
e authors assume that it will be possible to identify automated and manual controls by means of statistical
analyses.
Window state and blind status changes were recorded by the BMS and are available with their original time
stamp. Blind events are all changes, i.e. also changes of blind position e.g. between 20 and 80% closing.
Note that the ocial monitoring period by the original research team ended in October 2006. Aer that, data
was still automatically received, but the status of sensors not checked anymore. erefore, the number of sensors
having failures and not providing data continuously increases, which needs to be considered when using data
points aer 2006.

Custom code was used to validate the incoming data from the BMS for completeness and validity. e code
had been very specic according to the system conguration and is not available anymore. Its value for future
applications or future data usage would be very low because 90% of the code was written to check the syntactically
correctness of the data. While the authors expected such syntactical correctness being granted for data exported
from a BMS, the rst month of monitoring (not included in the database) showed several problems with the
structure of the data, which required many lines of custom code, very specic to the BMS in place and therefore
not generalizable to any other application.
Received: 8 May 2019; Accepted: 14 August 2019;
Published: xx xx xxxx

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
is research was supported by the German Federal Ministry of Economics and Technology (BMWi) with the
project ID’s 0335007 F and 03ET1289B.

All authors provided feedback on all steps, especially critical feedback on the paper. M.S. prepared the data
repository and wrote the paper. M.K. was involved in the data collection, data preparation and writing of the
paper. A.W. was involved in the data collection and writing of the paper.

e authors declare no competing interests.
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