Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Makonin, Stephen"'
Autor:
Tu, Wen Ting Maria, Makonin, Stephen
As part of the investigation done by the IEEE Standards Association P2957 Working Group, called Big Data Governance and Metadata Management, the use of persistent identifiers (PIDs) is looked at for tackling the problem of reproducible research and s
Externí odkaz:
http://arxiv.org/abs/2209.10475
Non-Intrusive Load Monitoring (NILM) is a field of research focused on segregating constituent electrical loads in a system based only on their aggregated signal. Significant computational resources and research time are spent training models, often
Externí odkaz:
http://arxiv.org/abs/2009.07756
Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field. In the field of Non-Intrusive Load Monitoring (NILM), performance evaluation can be conducted on real-world aggregate signals, provide
Externí odkaz:
http://arxiv.org/abs/2008.10985
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labele
Externí odkaz:
http://arxiv.org/abs/2007.13645
Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisatio
Externí odkaz:
http://arxiv.org/abs/2001.07708
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation pe
Externí odkaz:
http://arxiv.org/abs/1912.06200
Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the har
Externí odkaz:
http://arxiv.org/abs/1907.06299
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems;
Externí odkaz:
http://arxiv.org/abs/1902.08736
Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms which make use
Externí odkaz:
http://arxiv.org/abs/1705.05767
Ubiquitous technology platforms have been created to track and improve health and fitness; similar technologies can help individuals monitor and reduce their carbon footprints. This paper proposes CarbonKit, a platform combining technology, markets,
Externí odkaz:
http://arxiv.org/abs/1608.04162