Published September 19, 2024 | Version v1
Dataset Open

Data Set for Probabilistic Indoor Temperature Forecasting

Authors/Creators

  • 1. ROR icon HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung

Description

1. Dataset Manifest

This text provides a description of the dataset used for model training and evaluation in our study "A Tutorial on Deep Learning for Probabilistic Indoor Temperature Forecasting". The dataset consists of various simulated thermal and environmental parameters for different room configurations. Below, you will find a table detailing each column in the dataset along with its description and unit of measurement. 

1.1. Columns Description

Column Name Description Unit
time Time stamp of the measurement -
ZweiPersonenBuero.TAir Air temperature inside a two-person office °C
heatStat.Heat.Q_flow Heating rate in the room W
weaDat.AirPressure Atmospheric pressure Pa
weaDat.AirTemp Outside air temperature °C
weaDat.SkyRadiation Longwave sky radiation W/m²
weaDat.TerrestrialRadiation Terrestrial radiation W/m²
weaDat.WaterInAir Absolute humidity g/kg
VAir Air volume in the room m³
AExt0 Exterior wall area facing the south m²
AExt1 Exterior wall area facing the north m²
AInt Total interior wall area m²
AFloor Floor area of the room m²
AWin0 Window area facing the south m²
AWin1 Window area facing the north m²
azi0 Azimuth (direction) of the first exterior wall rad
azi1 Azimuth (direction) of the second exterior wall rad
id Unique identifier for the room configuration -
is_holiday Indicator whether the day is a holiday (1 for yes, 0 for no) -

1.2. Note on Multi-Value Columns

For rooms with multiple exterior walls (rooms 15-30):

  • AExt: {Exterior wall 1 area, Exterior wall 2 area}
  • AWin: {Window area on exterior wall 1, Window area on exterior wall 2}
  • azi: {Azimuth of exterior wall 1, Azimuth of exterior wall 2}

Example:

  • AExt = {10, 15}
  • AWin = {2, 0}
  • azi = {0, 3.1415}

This indicates two exterior walls with areas of 10 m² and 15 m² facing south (0 rad) and north (3.1415 rad), respectively. The south-facing wall has a window of 2 m², while the north-facing wall has no window.

1.3. Data Sources

  • Room Model: Simulated using the reduced-order package of the Modelica Buildings Library.
  • Weather Data: Provided by the German Meteorological Service (DWD) in Test Reference Year (TRY) format.

This comprehensive dataset provides crucial parameters required to train and evaluate thermal models for different room configurations. The simulation data ensures a diverse range of environmental and occupancy conditions, enhancing the robustness of the models.

1.4. Data scaling

The data set contains the raw data as well as the scaled data used for training and testing the model. The scaling was carried out using the StandardScaler package.

1.5. Weather data license

This data set contains weather data recorded by the DWD under license „Datenlizenz Deutschland – Namensnennung – Version 2.0" (URL). The data is provided by "Bundesinstitut für Bau-, Stadt- und Raumforschung". The data can be downloaded from here. We use data from the year 2015 from Heilbronn. We have added the weather data to the data set unchanged.

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