2篇
您的检索式：作者名="JIM Higgins"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 昆士兰急救部介绍(英文)显示文摘昆士兰急救部 (QAS)是从 1892年时的一个单纯的病人运输机构 ,发展成为世界上第四大急救中心 ,拥有世界一流的院前急救的专业技术人员。QAS和昆士兰科技大学 (QUT)合作培养急救的专业人员和管理专家。因为QAS的急救服务不是一个“使用者支付”的系统 ,所以它能保证急救服务于每个人。QAS作为一个政府机构 ,在它的系统组成 ,功能分布 ,人力资源分配以及发展过程方面 。 | JIM Higgins 侯香玉 | 2005 | 中华急诊医学杂志2005，14，1： | 3 |
| 2 | Prediction of voltage distribution using deep learning and identified key smart meter locations显示文摘The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers. | Maizura Mokhtar Valentin Robu David Flynn Ciaran Higgins Jim Whyte Caroline Loughran Fiona Fulton | 2021 | Energy and AI2021，6，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员