@ARTICLE{Włostowska_Sandra_Description_Early,
 author={Włostowska, Sandra and Kawa, Bartłomiej and Borkowski, Piotr},
 pages={e153832},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 howpublished={online},
 year={Early Access},
 abstract={The article compares selected classification algorithms and those dedicated to anomaly detection. The models used temperature measurements in four rooms simulated in the Matlab Simscape environment as test signals. The empirical part of the work consists of two parts. In the first one, an example data from the simulated building heating model object, models were built using unsupervised and supervised machine learning algorithms. Then, data from the facility was collected again with changed parameters (failures occurred at times other than the test ones, and the temperature patterns differed from those recorded and used to train the models). The algorithms' effects and test signals (temperature changes) were saved in the database. The obtained results were presented graphically in the Grafana program. The second part of the work presents a solution in which the analysis of the operating status of the heating system takes place in real time. Using an OPC server, data was exchanged between the Matlab environment and the database installed on a virtual machine in the Ubuntu system. The conclusions present the results and collect the authors' suggestions regarding the practical applications of the discussed classification models.},
 title={Description and comparison of fault detection algorithms based on a selected building automation device},
 type={Article},
 URL={http://czasopisma.pan.pl/Content/134090/PDF-MASTER/BPASTS-04746-EA.pdf},
 doi={10.24425/bpasts.2025.153832},
 keywords={fault detection, machine learning, building automation, smart-metering},
}