Zobrazeno 1 - 10
of 14
pro vyhledávání: '"D. W. Wanik"'
Autor:
A. G. Cosby, V. Lebakula, C. N. Smith, D. W. Wanik, K. Bergene, A. N. Rose, D. Swanson, D. E. Bloom
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Current human population growth along Earth’s coasts is on a collision path with anticipated consequences of increasing natural and anthropogenic induced coastal hazards. Using recently-available ambient, dasymetric data, we developed meth
Externí odkaz:
https://doaj.org/article/45e8c20b404b484a9bd33090c405dc8d
Autor:
Jun Yan, Jesse O. Bash, Valerie Garcia, Christina Feng Chang, Penny Vlahos, D. W. Wanik, Marina Astitha, Chunling Tang
Publikováno v:
J Great Lakes Res
Eutrophication and excessive algal growth pose a threat on aquatic organisms and the health of the public, environment, and the economy. Understanding what drives excessive algal growth can inform mitigation measures and aid in advance planning to mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13fe1469b60564a9fcf7a6cd427b9a55
https://doi.org/10.1016/j.jglr.2021.09.011
https://doi.org/10.1016/j.jglr.2021.09.011
Autor:
D. W. Wanik, Berk A. Alpay, Guannan Liang, Diego Cerrai, Peter Watson, Emmanouil N. Anagnostou
Publikováno v:
Forecasting, Vol 2, Iss 8, Pp 151-162 (2020)
Forecasting
Volume 2
Issue 2
Pages 8-162
Forecasting
Volume 2
Issue 2
Pages 8-162
Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the ent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46ca2ae38d77bf0a4cd08e218444667d
https://doi.org/10.3390/forecast2020008
https://doi.org/10.3390/forecast2020008
Autor:
Diego Cerrai, Emmanouil N. Anagnostou, Abul Ehsan Bhuiyan, Jaemo Yang, M. E. Frediani, D. W. Wanik, Xinxuan Zhang
Publikováno v:
IEEE Access, Vol 7, Pp 29639-29654 (2019)
This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8677d76f88734aea9b475be0bbd106d6
https://doi.org/10.1109/access.2019.2902558
https://doi.org/10.1109/access.2019.2902558
Publikováno v:
Sustainability
Volume 12
Issue 4
Sustainability, Vol 12, Iss 4, p 1525 (2020)
Volume 12
Issue 4
Sustainability, Vol 12, Iss 4, p 1525 (2020)
A growing number of electricity utilities use machine learning-based outage prediction models (OPMs) to predict the impact of storms on their networks for sustainable management. The accuracy of OPM predictions is sensitive to sample size and event s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b626d2b6e56a5efcec7142dca23230f8
Autor:
Brian M. Hartman, J. He, Emmanouil N. Anagnostou, Marina Astitha, Diego Cerrai, M. E. Frediani, Jaemo Yang, D. W. Wanik, Gary M. Lackmann
Publikováno v:
Journal of Applied Meteorology and Climatology. 57:51-79
Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut’s overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e367236b3dfe79d38746bd23459f2f85
https://doi.org/10.1175/jamc-d-16-0408.1
https://doi.org/10.1175/jamc-d-16-0408.1
Publikováno v:
Electric Power Systems Research. 146:236-245
The interaction of severe weather, overhead electric infrastructure and surrounding vegetation contributes to power outages. Given that 90% of storm outages in Connecticut are tree-related, accurate modeling of power outages before a storm arrives co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3977b169a8a86dfcd3cac207c3818022
https://doi.org/10.1016/j.epsr.2017.01.039
https://doi.org/10.1016/j.epsr.2017.01.039
Publikováno v:
UEMCON
Emergency managers at electric distribution utilities benefit from tools that help estimate the impacts of weather on their infrastructure networks when communicating with customers and regulators. In this paper, we adopted deep learning methods - Lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76ea6790cdc48ef860641b8783098bac
https://doi.org/10.1109/uemcon47517.2019.8992951
https://doi.org/10.1109/uemcon47517.2019.8992951
Autor:
Osvaldo Pensado, Amvrossios C. Bagtzoglou, D. W. Wanik, Hao Yuan, Jintao Zhang, William Hughes, Wei Zhang
Publikováno v:
Reliability Engineering & System Safety. 207:107367
Several disastrous storms, such as Hurricane Sandy in 2012, that brought massive area power outages for several days and even weeks in some areas, highlight the necessity of enhancing the physical power distribution system, including the pole-wire ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::728a66edf1a825fcb23d7ad2ad5cd55b
https://doi.org/10.1016/j.ress.2020.107367
https://doi.org/10.1016/j.ress.2020.107367
Publikováno v:
Infrastructures, Vol 3, Iss 3, p 33 (2018)
Infrastructures
Volume 3
Issue 3
Infrastructures
Volume 3
Issue 3
Extreme weather can cause severe damage and widespread power outages across utility service areas. The restoration process can be long and costly and emergency managers may have limited computational resources to optimize the restoration process. Thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::479dc040ba750f053fcabf2bc96d0b9c
http://www.mdpi.com/2412-3811/3/3/33
http://www.mdpi.com/2412-3811/3/3/33