[HTML][HTML] Building power consumption datasets: Survey, taxonomy and future directions
In the last decade, extended efforts have been poured into energy efficiency. Several energy
consumption datasets were henceforth published, with each dataset varying in properties …
consumption datasets were henceforth published, with each dataset varying in properties …
[HTML][HTML] Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
Providing the user with appliance-level consumption data is the core of each energy
efficiency system. To that end, non-intrusive load monitoring is employed for extracting …
efficiency system. To that end, non-intrusive load monitoring is employed for extracting …
A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids
Smart grid technology increases reliability, security, and efficiency of the electrical grids.
However, its strong dependencies on digital communication technology bring up new …
However, its strong dependencies on digital communication technology bring up new …
[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 …
Transfer learning for multi-objective non-intrusive load monitoring in smart building
Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon
emissions in buildings is of importance to greenhouse gas emissions reductions. This …
emissions in buildings is of importance to greenhouse gas emissions reductions. This …
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
The machine fault diagnosis is being considered in a larger-scale complex system with
numerous measurements from diverse subsystems or components, where the collected data …
numerous measurements from diverse subsystems or components, where the collected data …
Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour?
Identification of faulty appliance behaviour in real time can signal energy wastage and the
need for appliance servicing or replacement leading to energy savings. The problem of …
need for appliance servicing or replacement leading to energy savings. The problem of …
A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network
Y Yang, H Zheng, Y Li, M Xu, Y Chen - ISA transactions, 2019 - Elsevier
Fault diagnosis of rotating machinery is crucial to improve safety, enhance reliability and
reduce maintenance cost. The manual feature extraction and selection of traditional fault …
reduce maintenance cost. The manual feature extraction and selection of traditional fault …
[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 …
An overview of non-intrusive load monitoring: Approaches, business applications, and challenges
Load Monitoring (LM) is a fundamental step to implement effective energy management
schemes. LM includes Intrusive LM (ILM) and Non-Intrusive LM (NILM). Compared with …
schemes. LM includes Intrusive LM (ILM) and Non-Intrusive LM (NILM). Compared with …