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Energies
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Challenge and Research Trends of Smart Power Grid
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Challenge and Research Trends of Smart Power Grid

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 August 2021) | Viewed by 2492
Submit your paper and select the Journal "Energies" and the Special Issue "Challenge and Research Trends of Smart Power Grid" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the guest editor or the journal editor ([email protected]) for any queries.

Special Issue Editor

Prof. Dr. Young-Chon Kim

E-Mail Website
Guest Editor
Division of Electronic and Information, Department of Information Technology, Jeonbuk National University, Jeonju 54896, Korea
Interests: Communication technologies and rchitectures for smart power grid; next generation; energy management system; energy load forecasting

Special Issue Information

Dear Colleagues,

The smart power grid is the future electric power system that supports bi-directional energy and information flow between consumer and service provider. The smart power grid aims to modernize the current electric power system with a new set of technologies and services such as distributed energy generation (wind and solar energy), advanced meter infrastructures, electric vehicles, and home/building energy management systems. Artificial intelligence, Blockchain technology, and the underlying communication infrastructures will play an important role in supporting the grid integration of different smart grid applications with the aim to improve power quality, reliability, efficiency, and security.

This Special Issue focuses on the role of information and communication technologies, artificial intelligence, Blockchain technology, and their applications in the development of the future smart power grid including energy forecasting, energy trading, the integration of wind energy, solar energy, electric vehicles, smart meters, smart homes/building, etc.

Prof. Dr. Young-Chon Kim
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

  • Communication Technologies and Architectures
  • Artificial Intelligence
  • Blockchain
  • Wind Power Farms
  • Photovoltaic Power System
  • Electric Vehicles
  • Smart Meter Infrastructures
  • Energy Load Forecasting
  • Next Generation Energy Management System, etc.

Published Papers (1 paper)

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Research

23 pages, 38919 KiB  
Article
Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System
by Faisal Mohammad, Mohamed A. Ahmed and Young-Chon Kim
Energies 2021, 14(19), 6161; https://doi.org/10.3390/en14196161 - 27 Sep 2021
Cited by 10 | Viewed by 1807
Abstract
An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, [...] Read more.
An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets. Full article
(This article belongs to the Special Issue Challenge and Research Trends of Smart Power Grid)
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