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Energies
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Green Network Technologies and Renewable Energy Systems
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Green Network Technologies and Renewable Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 11421

Special Issue Editor

Prof. Dr. Xavier Hesselbach

E-Mail Website
Guest Editor
Department of Network Engineering, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain
Interests: network virtualization; network softwarization; green energy; network services; network aware energy management; critical networks; networks slicing; SDN/NFV; 5G technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of “Green Network Technologies and Renewable Energy Systems”.

The new generation of networks enables the massive connectivity of devices, increases the capacity and guarantees the latency. 5G, IoT and virtualization technologies promise new energy aware applications in a broad range of fields and verticals, such as smart cities, smart homes, industrial environments or energy providers. It is expected the support for real time negotiations between the consumer and the energy suppliers about the energy demands and the availability, considering the mixing of renewal and non-renewal sources. In the future it is expected the need of full energy renewal ecosystems. In this field, Artificial Intelligence (AI) strategies are a relevant topic towards the intelligentization of the network. Security and privacy are also relevant topics in terms of the information exchanged in the ecosystem.

This Special Issue will deal with novel architectures, strategies, control and optimization for Green Efficient Energy Consumption. Topics of interest for publication include, but are not limited to:

  • Architectures, infrastructure and systems.
  • Energy policies for efficient energy consumption.
  • Security and privacy for energy aware communications.
  • Modelling, performance analysis and optimization.
  • Energy aware services orchestration.
  • Usage scenarios, testbeds and experimental prototypes.
  • Full renewal energy ecosystems strategies and management.
  • Machine learning and big data analytics for the ecosystem.
  • Network slicing for green efficient energy consumption.

Prof. Xavier Hesselbach
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Energy efficiency
  • energy management
  • demand response
  • green energy
  • orchestration
  • renewal energy ecosystems
  • NFV
  • SDN
  • machine learning
  • network slicing
  • optimization
  • architectures
  • policies
  • modelling
  • performance evaluation

Published Papers (4 papers)

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Research

25 pages, 1826 KiB  
Article
Energy-Efficient and Disjoint Multipath Using Face Routing in Wireless Sensor Networks
by Hyunchong Cho, Seungmin Oh, Yongje Shin and Euisin Lee
Energies 2021, 14(22), 7823; https://doi.org/10.3390/en14227823 - 22 Nov 2021
Cited by 1 | Viewed by 1522
Abstract
In WSNs, multipath is well-known as a method to improve the reliability of packet delivery by making multiple routes from a source node to a destination node. To improve reliability and load-balancing, it is important to ensure that disjoint characteristics of multipath do [...] Read more.
In WSNs, multipath is well-known as a method to improve the reliability of packet delivery by making multiple routes from a source node to a destination node. To improve reliability and load-balancing, it is important to ensure that disjoint characteristics of multipath do not use same nodes during path generation. However, when multipath studies encounter a hole area from which is hard to transmit data packets, they have a problem with breaking the disjoint features of multipath. Although existing studies propose various strategies to bypass hole areas, they have side effects that significantly accelerate energy consumption and packet transmission delay. Therefore, to retain the disjoint feature of multipath, we propose a new scheme that can reduce delay and energy consumption for a node near a hole area using two approaches—global joint avoidance and local avoidance. This scheme uses global joint avoidance to generate a new path centered on a hole area and effectively bypasses the hole area. This scheme also uses local joint avoidance that does not select the same nodes during new path generation using a marking process. In simulations, the proposed scheme has an average 30% improvement in terms of average energy consumption and delay time compared to other studies. Full article
(This article belongs to the Special Issue Green Network Technologies and Renewable Energy Systems)
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31 pages, 4412 KiB  
Article
Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis
by Ahmed Abdelaziz, Vitor Santos and Miguel Sales Dias
Energies 2021, 14(22), 7810; https://doi.org/10.3390/en14227810 - 22 Nov 2021
Cited by 17 | Viewed by 3636
Abstract
The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the [...] Read more.
The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research. Full article
(This article belongs to the Special Issue Green Network Technologies and Renewable Energy Systems)
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19 pages, 3062 KiB  
Article
Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case
by Michał Sabat and Dariusz Baczyński
Energies 2021, 14(11), 3204; https://doi.org/10.3390/en14113204 - 30 May 2021
Cited by 1 | Viewed by 2377
Abstract
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate [...] Read more.
Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%. Full article
(This article belongs to the Special Issue Green Network Technologies and Renewable Energy Systems)
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9 pages, 224 KiB  
Article
Predicting Renewable Energy Investment Using Machine Learning
by Govinda Hosein, Patrick Hosein, Sanjay Bahadoorsingh, Robert Martinez and Chandrabhan Sharma
Energies 2020, 13(17), 4494; https://doi.org/10.3390/en13174494 - 31 Aug 2020
Cited by 6 | Viewed by 2854
Abstract
In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from [...] Read more.
In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from reduced fossil fuel dependence and the fluctuations associated with imported fuel prices. However, numerous countries have not yet made preparations to increase RE production and integration. In many instances, this reluctance seems to be predominant in energy-rich countries, which typically provide heavy subsidies on electricity prices. With such subsidies, there is no incentive to invest in RE since the time taken to recoup such investments would be significant. We develop a model using a Neural Network (NN) regression algorithm to quantitatively illustrate this conjecture and also use it to predict the reduction in electricity price subsidies required to achieve a specified RE production target. The model was trained using 10 leading metrics from 53 countries. It is envisaged that policymakers and researchers can use this model to plan future RE targets to satisfy the Nationally Determined Contributions (NDC) and determine the required electricity subsidy reductions. The model can easily be modified to predict what changes in other country factors can be made to stimulate growth in RE production. We illustrate this approach with a sample use case. Full article
(This article belongs to the Special Issue Green Network Technologies and Renewable Energy Systems)
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