Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
Y Himeur, A Alsalemi, F Bensaali… - … Journal of Intelligent …, 2022 - Wiley Online Library
Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …
observing power usage in buildings. It tackles several challenges in transitioning into a more …
Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
A residential labeled dataset for smart meter data analytics
Smart meter data is a cornerstone for the realization of next-generation electrical power
grids by enabling the creation of novel energy data-based services like providing …
grids by enabling the creation of novel energy data-based services like providing …
Watt's up at home? Smart meter data analytics from a consumer-centric perspective
The key advantage of smart meters over traditional metering devices is their ability to
transfer consumption information to remote data processing systems. Besides enabling the …
transfer consumption information to remote data processing systems. Besides enabling the …
基于谱聚类和LSTM 神经网络的电动公交车充电负荷预测方法
王哲， 万宝， 凌天晗， 董晓红， 穆云飞， 邓友均… - 电力建设, 2021 - epjournal.csee.org.cn
目前电动公交车的渗透率较大, 且充电频率和充电量较高, 故而其充电负荷对电网运行与调度
产生着不可忽略的影响. 因此, 电动公交车的充电负荷预测研究具有重要的理论与现实意义 …
产生着不可忽略的影响. 因此, 电动公交车的充电负荷预测研究具有重要的理论与现实意义 …
KNN-SC: novel spectral clustering algorithm using k-nearest neighbors
Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral
clustering has several desirable advantages (such as the capability of discovering non …
clustering has several desirable advantages (such as the capability of discovering non …
An approach of electrical load profile analysis based on time series data mining
Y Shi, TAO Yu, Q Liu, H Zhu, F Li, Y Wu - IEEE Access, 2020 - ieeexplore.ieee.org
In the current electrical load profile analysis, considering the shortage of traditional methods
on the typical load profile extraction of single consumers and the load profile feature …
on the typical load profile extraction of single consumers and the load profile feature …
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach
X Zhou, J Feng, J Wang, J Pan - PeerJ Computer Science, 2022 - peerj.com
Load forecasting is very essential in the analysis and grid planning of power systems. For
this reason, we first propose a household load forecasting method based on federated deep …
this reason, we first propose a household load forecasting method based on federated deep …
Deep Neural Network Based Methodology for Very-Short-Term Residential Load Forecasting
R Gonzalez, S Ahmed… - 2022 13th International …, 2022 - ieeexplore.ieee.org
Residential load forecasting has long been a prediction problem due to high uncertainty
associated with a single electricity consumer. Predicting residential load demand is …
associated with a single electricity consumer. Predicting residential load demand is …
Clustering Models for Demand Response Aggregation
In the modern power system, the need for flexibility is increasing manifold due to the high
quantum of renewable energy. The intermittency in renewable generation combined with …
quantum of renewable energy. The intermittency in renewable generation combined with …