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Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO

  • The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 % and a specificity of at least 97 % with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable
Metadaten
Author:Michael Parzinger, Lucia Hanfstaengl, Ferdinand Sigg, Uli Spindler, Ulrich Wellisch, Markus Wirnsberger
DOI:https://doi.org/10.1016/j.buildenv.2022.109313
Parent Title (English):Building and Environment
Document Type:Article (peer reviewed)
Language:English
Publication Year:2022
Tag:LASSO; Occupancy detection; Random Forest; Simulated learning data
Volume:223
First Page:109313
Peer reviewed:Ja
Project Title:ENOB: In Situ Nachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex71
Acronym:In Situ Nachweisverfahren
Funding Agency:Bundesministerium für Wirtschaft und Energie
Grant Number:03ET1509C
faculties / departments:Fakultät für Angewandte Natur- und Geisteswissenschaften