TY - GEN
T1 - From electricity and water consumption data to information on office occupancy
AU - Stjelja, Davor
AU - Jokisalo, Juha
AU - Kosonen, Risto
PY - 2021
Y1 - 2021
N2 - Climate change and technology development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence which would offer new insights to the building’s operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.
AB - Climate change and technology development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence which would offer new insights to the building’s operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.
UR - https://www.sisailmayhdistys.fi/Julkaisut/Sisailmastoseminaari-julkaisut
M3 - Conference article in proceedings
SN - 978-952-5236-52-1
T3 - Sisäilmayhdistys raportti
SP - 113
EP - 118
BT - Sisäilmastoseminaari 2021. Verkkoseminaari 9.3.2021
A2 - Ahola, Mervi
A2 - Merikari, Anna
PB - SIY Sisäilmatieto Oy
T2 - Sisäilmastoseminaari
Y2 - 9 March 2021 through 9 March 2021
ER -