.png){kind=icon}
返回
Load Disaggregation Based on Time Window for HEMS Application
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3078340.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.
Keyword:
Hidden Markov models
Principal component analysis
Monitoring
Performance evaluation
Home appliances
Aggregates
Power measurement
Artificial intelligence in power systems
load disaggregation
PCA
time window
machine learning algorithms
HEMS
NILM
RAE dataset
REDD dataset
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation.png)
SUSTAINABILITYOAAI
SUSTAINABILITYIF3.3
OAAI分享收藏分享收藏On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing在使用图形信号处理进行非侵入式设备负载监控的无训练解决方案上IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容