Results 111 to 120 of about 2,135 (206)
The increasing need for efficient energy consumption monitoring, driven by economic and environmental concerns, has made Non-Intrusive Load Monitoring (NILM) a cost-effective alternative to traditional measurement methods.
Carlos Rodriguez-Navarro +3 more
doaj +1 more source
Approaches to Non-Intrusive Load Monitoring (NILM) in the Home
When designing and implementing an intelligent energy conservation system for the home, it is essential to have insight into the activities and actions of the occupants. In particular, it is important to understand what appliances are being used and when. In the computational sustainability research community this is known as load disaggregation or Non-
openaire +1 more source
Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems.
Siqi Liu, Zhiyuan Xie, Zhengwei Hu
openaire +1 more source
Non-Intrusive Load Monitoring (NILM) enables appliance-level energy analysis from aggregated electrical signals, offering valuable insights for smart energy systems. While most NILM research focuses on high-resource environments, this study evaluates the feasibility of deploying NILM algorithms on constrained edge computing platforms.
David Serna +4 more
openaire +1 more source
A NILM load identification method based on structured V-I mapping. [PDF]
Du Z, Yin B, Zhu Y, Huang X, Xu J.
europepmc +1 more source
Non-Intrusive Load Monitoring (NILM): combining multiple distinct electrical features and unsupervised machine learning techniques [PDF]
Dissertation, Universität Duisburg-Essen ...
openaire +1 more source
DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model. [PDF]
Sun R, Dong K, Zhao J.
europepmc +1 more source
Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition. [PDF]
Franco P +3 more
europepmc +1 more source
A Hybrid Federated Learning Framework for Enhancing Privacy and Robustness in Non-Intrusive Load Monitoring. [PDF]
Rong J, Zhou Q, Wu H.
europepmc +1 more source

