Zobrazeno 1 - 6
of 6
pro vyhledávání: '"David W, Wanik"'
Publikováno v:
IEEE Access, Vol 12, Pp 31824-31840 (2024)
This study aims to develop models for predicting hourly energy demand in the State of Connecticut, USA from 2011 to 2021 using machine learning algorithms inputted with airport weather stations’ data from the Automated Surface Observing System (ASO
Externí odkaz:
https://doaj.org/article/84da67d9fa834b3e9a82bd1d0bf74573
Autor:
Diego Cerrai, David W. Wanik, Md Abul Ehsan Bhuiyan, Xinxuan Zhang, Jaemo Yang, Maria E. B. Frediani, Emmanouil N. Anagnostou
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://doaj.org/article/1ae0567f09f14719905574667b9cb0da
Publikováno v:
The Journal of Engineering (2020)
The outage prediction model (OPM) is a weather-related machine learning-based power outage model, which has been developed at the University of Connecticut for many years and has recently grown to cover three states and five utility service territori
Externí odkaz:
https://doaj.org/article/52aede51e5bd4ca18bcd7e8a33da780d
Publikováno v:
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC).
Publikováno v:
Remote Sensing, Vol 9, Iss 3, p 286 (2017)
Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the S
Externí odkaz:
https://doaj.org/article/0137db0893694d6e914824b1b3d150d6
Autor:
Jichao, He, David W, Wanik, Brian M, Hartman, Emmanouil N, Anagnostou, Marina, Astitha, Maria E B, Frediani
Publikováno v:
Risk analysis : an official publication of the Society for Risk Analysis. 37(3)
This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estim