Special Issue "Internet of Energy (IoE): New Business Scenarios, Technologies and Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editors

Prof. Dr. Hamid Doost Mohammadian
E-Mail Website
Guest Editor
Department of Business and Economics, University of Applied Sciences (FHM), Bielefeld, Germany
Interests: energy management; modeling; blue-green economy; industry 4.0; Internet of Things; Internet of Energy; digitalization; sustainability; international management; vocational training, education, business; CSR; SME management; cultural dimensions; 5th wave theory/tomorrow age; future studies
Special Issues and Collections in MDPI journals
Prof. Dr. Dario Assante
E-Mail Website1 Website2
Guest Editor
Faculty of Engineering, Università Telematica Internazionale Uninettuno, 00186 Rome, Italy
Interests: electromagnetic modeling; electromagnetic compatibility; shielding; energy management; smart grids; Internet of Things
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The digital transformation driven by 4.0 technologies is having a disruptive impact on many sectors, including energy. These innovations allow us to respond to both the growing energy demand and the lower carbon emission requirements. The application of Internet of Things (IoT) technologies to vast types of devices has led to electrical networks being monitored and controllable in a capillary way. Big data techniques and predictive models allow smart management of networks, from a national scale up to micronetworks, facilitating the spread of distributed generation and storage systems. New intermediaries and new business models appear in the energy market. The energy economy has changed from a power economy to a data and power economy, leading to the concept of the “Internet of Energy”.

The aim of this Special Issue is to report on new scenarios, technologies, and applications related to the concept of the Internet of Energy and discuss challenges and risks. Examples of this work include the description of new business models and new operating methodologies related to the energy sector, the impact of the digital transformation on the players of the energy sector, new policies and strategies for the monitoring and control of macro and microenergy grids, and new models of data monetization in the energy sector. The Special Issue also aims to cover the impact of 4.0 technologies on the energy sector, including the large-scale deployment of IoT, the employment of big data and machine learning for energy forecasting, the use of cloud platforms for the control of smart grids, and the new cyber-risks for the energy sector.

Technology development has led to new opportunities for business improvement. Internet of Things, Internet of Energy, cyberphysical systems, big data, and machine learning are new techniques used in Industry 4.0 that enable businesses to better manage resources and provide them with the flexibility to respond to business conditions.

Prof. Dr. Hamid Doost Mohammadian
Prof. Dr. Dario Assante
Guest Editors

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. Electronics 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 1800 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

  • IoT (Internet of Things)
  • IoE (Internet of Energy)
  • energy management
  • smart grids
  • smart metering
  • digital transformation

Published Papers (1 paper)

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Research

Article
Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System
Electronics 2021, 10(9), 1026; https://doi.org/10.3390/electronics10091026 - 25 Apr 2021
Viewed by 505
Abstract
With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most [...] Read more.
With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most challenging forecasting tasks as it requires high accuracy and stable operating conditions. In this study, we propose a novel multi-behavior with bottleneck features long short-term memory (LSTM) model that combines the predictive behavior of long-term, short-term, and weekly feature models by using the bottleneck feature technique for building energy management systems. The proposed model, along with the unique scheme, provides predictions with the accuracy of long-term memory, adapts to unexpected and unpatternizable intrinsic temporal factors through the short-term memory, and remains stable because of the weekly features of input data. To verify the accuracy and stability of the proposed model, we present and analyze several learning models and metrics for evaluation. Corresponding experiments are conducted and detailed information on data preparation and model training are provided. Relative to single-model LSTM, the proposed model achieves improved performance and displays an excellent capability to respond to unexpected situations in building energy management systems. Full article
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