Abstract
Non-intrusive monitoring of electrical loads (NILM) implemented by the state analysis method critically depends on the selection of appropriate features to identify devices. The commonly used expert selection is not optimal, and computational methods of feature selection require the establishment of an optimisation criterion that will ensure a satisfactory level of NILM system performance. An important element of the discussed method is the selection of the classifier and its matching with the selection method to construct a softsensor. In this work, four feature selection methods (Boruta, ReliefF, mRMR, the author's method) and four classifiers (decision trees, random forests, artificial neural networks and a hybrid classifier) were implemented and tested. Software was implemented for the softsensor architectures tested, enabling the verification of optimal configurations for NILM. The research confirmed that the selection of features using optimisation methods and the use of a softsensor allow for better support in the decision-making process.
Paper Type
Full Paper
DOI
10.62036/ISD.2025.78
Supporting consumer decision-making by a softsensors with classifiers in an optimized feature space
Non-intrusive monitoring of electrical loads (NILM) implemented by the state analysis method critically depends on the selection of appropriate features to identify devices. The commonly used expert selection is not optimal, and computational methods of feature selection require the establishment of an optimisation criterion that will ensure a satisfactory level of NILM system performance. An important element of the discussed method is the selection of the classifier and its matching with the selection method to construct a softsensor. In this work, four feature selection methods (Boruta, ReliefF, mRMR, the author's method) and four classifiers (decision trees, random forests, artificial neural networks and a hybrid classifier) were implemented and tested. Software was implemented for the softsensor architectures tested, enabling the verification of optimal configurations for NILM. The research confirmed that the selection of features using optimisation methods and the use of a softsensor allow for better support in the decision-making process.
Recommended Citation
Bartman, J., Twarog, B., Kwiatkowski, B., Kwater, T. & Hawro, P. (2025). Supporting consumer decision-making by a softsensors with classifiers in an optimized feature spaceIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.78