[HTML][HTML] A systematic review of machine learning techniques related to local energy communities
In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …
processes in several sectors, as in the case of electrical power systems. Machine learning …
Data poisoning attacks on federated machine learning
Federated machine learning which enables resource-constrained node devices (eg, Internet
of Things (IoT) devices and smartphones) to establish a knowledge-shared model while …
of Things (IoT) devices and smartphones) to establish a knowledge-shared model while …
What and how: generalized lifelong spectral clustering via dual memory
Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms,
and been successfully applied into various applications. We in this work explore the problem …
and been successfully applied into various applications. We in this work explore the problem …
[HTML][HTML] Advanced distribution measurement technologies and data applications for smart grids: A review
The integration of advanced measuring technologies in distribution systems allows
distribution system operators to have better observability of dynamic and transient events. In …
distribution system operators to have better observability of dynamic and transient events. In …
Deep learning-based real-time building occupancy detection using AMI data
Building occupancy patterns facilitate successful development of the smart grid by
enhancing building-to-grid integration efficiencies. Current occupancy detection is limited by …
enhancing building-to-grid integration efficiencies. Current occupancy detection is limited by …
Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data
Accurate household profiles (eg, house type, number of occupants) identification is the key
to the successful implementation of behavioral demand response. Currently, supervised …
to the successful implementation of behavioral demand response. Currently, supervised …
NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease
Coronary artery disease (CAD) is one of the main causes of cardiac death around the world.
Due to its significant impact on the society, early and accurate detection of CAD is essential …
Due to its significant impact on the society, early and accurate detection of CAD is essential …
Representative task self-selection for flexible clustered lifelong learning
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of
tasks depending on previous experiences, eg, knowledge library or deep network weights …
tasks depending on previous experiences, eg, knowledge library or deep network weights …
Privacy-preserving household characteristic identification with federated learning method
Understanding residential household characteristics is crucial for retailers to provide
customers personalized services. Current methods infer household characteristics from …
customers personalized services. Current methods infer household characteristics from …
Power consumption forecast model using ensemble learning for smart grid
The prediction of power consumption of smart meters plays a vital role in power distribution
and management in the smart grid, which depends on real-time and historical data …
and management in the smart grid, which depends on real-time and historical data …