Training load monitoring algorithms on highly sub-metered home electricity consumption data
Author:
Publisher
Tsinghua University Press
Subject
Multidisciplinary
Link
http://xplorestaging.ieee.org/ielx5/5971803/6072946/06073013.pdf?arnumber=6073013
Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. An artificial intelligence‐based non‐intrusive load monitoring of energy consumption in an electrical energy system using a modified K‐Nearest Neighbour algorithm;IET Smart Cities;2024-01-24
2. Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15
3. Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring;Sensors;2022-08-05
4. Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption;Energies;2022-06-14
5. Appliance Level Energy Characterization of Residential Electricity Demand: Prospects, Challenges and Recommendations;IEEE Access;2021
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