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Special Issue "Green Network Technologies and Renewable Energy Systems"

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

Deadline for manuscript submissions: closed (20 October 2021).

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 and Collections 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 papers will be 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 2000 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 (2 papers)

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Research

Article
Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case
Energies 2021, 14(11), 3204; https://doi.org/10.3390/en14113204 - 30 May 2021
Viewed by 676
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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Article
Predicting Renewable Energy Investment Using Machine Learning
Energies 2020, 13(17), 4494; https://doi.org/10.3390/en13174494 - 31 Aug 2020
Cited by 3 | Viewed by 861
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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