Cargando…
Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs
Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cos...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036679/ https://www.ncbi.nlm.nih.gov/pubmed/33806215 http://dx.doi.org/10.3390/s21072308 |
_version_ | 1783676966796263424 |
---|---|
author | Wan Ariffin, Wan Nur Suryani Firuz Zhang, Xinruo Nakhai, Mohammad Reza Rahim, Hasliza A. Ahmad, R. Badlishah |
author_facet | Wan Ariffin, Wan Nur Suryani Firuz Zhang, Xinruo Nakhai, Mohammad Reza Rahim, Hasliza A. Ahmad, R. Badlishah |
author_sort | Wan Ariffin, Wan Nur Suryani Firuz |
collection | PubMed |
description | Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs. |
format | Online Article Text |
id | pubmed-8036679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80366792021-04-12 Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs Wan Ariffin, Wan Nur Suryani Firuz Zhang, Xinruo Nakhai, Mohammad Reza Rahim, Hasliza A. Ahmad, R. Badlishah Sensors (Basel) Article Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs. MDPI 2021-03-25 /pmc/articles/PMC8036679/ /pubmed/33806215 http://dx.doi.org/10.3390/s21072308 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Wan Ariffin, Wan Nur Suryani Firuz Zhang, Xinruo Nakhai, Mohammad Reza Rahim, Hasliza A. Ahmad, R. Badlishah Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title | Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title_full | Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title_fullStr | Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title_full_unstemmed | Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title_short | Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs |
title_sort | online learning approach for predictive real-time energy trading in cloud-rans |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036679/ https://www.ncbi.nlm.nih.gov/pubmed/33806215 http://dx.doi.org/10.3390/s21072308 |
work_keys_str_mv | AT wanariffinwannursuryanifiruz onlinelearningapproachforpredictiverealtimeenergytradingincloudrans AT zhangxinruo onlinelearningapproachforpredictiverealtimeenergytradingincloudrans AT nakhaimohammadreza onlinelearningapproachforpredictiverealtimeenergytradingincloudrans AT rahimhaslizaa onlinelearningapproachforpredictiverealtimeenergytradingincloudrans AT ahmadrbadlishah onlinelearningapproachforpredictiverealtimeenergytradingincloudrans |