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Special Issue "Computing in Energy Management Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 1370

Special Issue Editor

Prof. Deok-Jai Choi
E-Mail Website
Guest Editor
School of Electronics and Computer ENG, Chonnam National University, 77, Yongbong-Gu, Gwangju 500-757, Korea
Interests: ubiquitous computing; advanced networking; Internet of Things; Smart Grid

Special Issue Information

Dear Colleagues,

An energy management system (EMS) is referred to as a platform to provide monitoring, analysis, and control capacities of energy demand and available energy sources, in a way that minimizes energy consumption and power system operation cost, improves power system reliability, efficiency, and security, as well as meets other service quality requirements.

In recent years, a variety of energy-related software applications have been developed for several purposes, e.g., energy consumption reporting in web applications or an onsite energy display, tracking, and trend analysis to identify cost-saving opportunities, real-time demand, and response via automated control or energy conservation module. Therefore, EMS must be able to deal with massive data volumes originating from smart monitoring and measurement sensors, which are deployed all over the smart grid network. Furthermore, the current EMS must undergo some improvement by adopting advanced computing techniques and technologies in order to:

  • Provide on-demand computing resources for energy data lake and processing;
  • Guarantee data privacy and security for information exchange and transfer;
  • Maintain reliable data communication for real-time energy control and monitoring;
  • Provide a service-oriented energy management platform with wide-ranging functionality;
  • Detect anomalies and communicates abnormal energy consumption patterns, etc.

In this Special Issue, both academic and industrial researchers are kindly invited to contribute and address advanced technologies and theories as well as the most recent developments on the subject area of “Computing in Energy Management Systems”. Your contribution may describe design models, test campaigns, case-studies, and technological applications of advanced computing techniques and technologies in any scale of EMS implementation.

I look forward to receiving your submissions.

Prof. Deok-Jai Choi
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 2200 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

  • Future computing and applications for EMS
    • Cloud computing
    • Fog computing
    • Mobile edge computing
    • Big data-oriented computing
    • Computing high speed sensing data
    • High-performance computing
  • Computational intelligence strategies for EMS
    • Cognitive computing
    • Neural computing
    • Intelligent computation
    • Intentional computing
    • Adaptive computation
  • Database and big data management for EMS
    • Big data processing
    • Graph database scheme
    • Data privacy and security
    • Reliable data transfer protocol
    • Machine-learning-based analysis
  • Next-generation software architecture for EMS
    • Service-oriented architecture
    • Microservice architecture
    • Software modularity
    • Application programming interface

Published Papers (1 paper)

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Research

Article
A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting
Energies 2022, 15(7), 2623; https://doi.org/10.3390/en15072623 - 03 Apr 2022
Cited by 1 | Viewed by 732
Abstract
The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term [...] Read more.
The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model. Full article
(This article belongs to the Special Issue Computing in Energy Management Systems)
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