Abstract
Climate change and technological 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 that 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.
Highlights
Buildings are responsible for approximately 36% of all CO2 emissions in the European Union and28% on the global level
Ground truth data on the occupancy was collected from the camera with people counting software in 30-min intervals, which was summed to hourly intervals so that it could be compared with the consumption dataset
The current study aimed to examine the possibility of utilising sub-metered electricity and water consumption with data mining methods to determine the occupancy of the office
Summary
Buildings are responsible for approximately 36% of all CO2 emissions in the European Union and28% on the global level. The EU has set the goal of developing a sustainable, competitive, secure, and decarbonised energy system by the year 2050 [1,2]. One of the ways of achieving this goal is through the digitalisation of energy systems and buildings. The EU expects that smart-ready energy systems and buildings are going to offer new opportunities for energy savings. In order to promote smart ready technologies for the building sector, the EU has introduced a smart readiness indicator (SRI). The purpose of SRI is to determine the capability of buildings in using the information and communication technologies to adapt the building operation to the needs of the occupants and the grid while improving the overall performance of the building [1]
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