[HTML][HTML] An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
T Todic, V Stankovic, L Stankovic - Applied Energy, 2023 - Elsevier
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
[HTML][HTML] An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring
Traditional electric energy systems are experiencing a major revolution and the main drivers
of this revolution are green transition and digitalization. In this paper, an advanced system …
of this revolution are green transition and digitalization. In this paper, an advanced system …
A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing
The dynamic nature of chemical processes and manufacturing environments, along with
numerous machines, their unique activity states, and mutual interactions, render challenges …
numerous machines, their unique activity states, and mutual interactions, render challenges …
[HTML][HTML] Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction
Recently, a growing interest has been dedicated towards developing and implementing low-
cost energy efficiency solutions in buildings. Accordingly, non-intrusive load monitoring has …
cost energy efficiency solutions in buildings. Accordingly, non-intrusive load monitoring has …
Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations
Y Liu, W Liu, Y Shen, X Zhao, S Gao - Applied Energy, 2021 - Elsevier
Non-intrusive load monitoring is a promising technology in intelligent energy consumption
management, which can provide insights for electricity use patterns and customer living …
management, which can provide insights for electricity use patterns and customer living …
Industrial load disaggregation based on hidden Markov models
Non-intrusive load monitoring (NILM) technology can identify the energy consumed by each
individual device from the aggregate electricity measurements, contributing to energy saving …
individual device from the aggregate electricity measurements, contributing to energy saving …
A scoping review of energy load disaggregation
BA Tolnai, Z Ma, BN Jørgensen - EPIA Conference on Artificial Intelligence, 2023 - Springer
Energy load disaggregation can contribute to balancing power grids by enhancing the
effectiveness of demand-side management and promoting electricity-saving behavior …
effectiveness of demand-side management and promoting electricity-saving behavior …
Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period
The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives,
including the way people interact with buildings. Leveraging the advances in machine …
including the way people interact with buildings. Leveraging the advances in machine …
[HTML][HTML] Robust event detection for residential load disaggregation
Nonintrusive load monitoring (NILM) can facilate the transition to energy-efficient and low-
carbon buildings. Event detection is the first and most critical step in event-based NILM and …
carbon buildings. Event detection is the first and most critical step in event-based NILM and …
Nonintrusive load monitoring based on self-supervised learning
Deep learning models for nonintrusive load monitoring (NILM) tend to require a large
amount of labeled data for training. However, it is difficult to generalize the trained models to …
amount of labeled data for training. However, it is difficult to generalize the trained models to …