Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Artificial Intelligence in Energy Management
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence in Energy Management

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 6037

Special Issue Editor

Prof. Dr. Satoru Okamoto

E-Mail Website
Guest Editor
Interdisciplinary Graduate School of Science and Engineering, Shimane University, Matsue 690-8504, Japan
Interests: advanced thermal and fluids science and technology: flow-induced vibrations; small-scale energy systems with gas turbines and heat pumps; experimental fluid dynamics; heat transfer; biomedical engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defense, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as energies. This Special Issue will provide information on innovation, research, development, and demonstration related to “Artificial Intelligence in Energy Management Systems.” The main focus of this Special Issue is artificial intelligence in conventional and non-conventional thermal energy management systems. Papers are solicited in areas including, but not limited to the following:

  • AI in energy management systems
  • AI in distributed energy systems
  • AI in renewable energy systems
  • AI in energy storage
  • Demand-side management (DSM) or demand-side response (DSR) by AI
  • Home energy management system (HEMS) by AI
  • Building and energy management system (BEMS) by AI
  • Smart energy management system by AI
  • Smart building by AI
  • Smart city by AI
  • Energy efficiency enhancement by AI
  • System performance improvement by AI
  • Energy modeling and simulation
  • Internet of Things (IoT)
  • Information and communication technology (ICT)
  • Virtual reality (VR), augmented reality (AR), and mixed reality (MR)
  • Big data
  • 5G
  • Smart grid
  • Intelligent control
  • Artificial intelligence (AI)
  • Machine learning
  • Deep learning

Authors are invited to contribute to increasing international cooperation, as well as the understanding and promotion of efforts and disciplines in the area of “Artificial Intelligence in Energy Management Systems.” The dissemination of knowledge by presenting research results, new developments, and novel concepts in “Artificial Intelligence in Energy Management Systems” will serve as the foundation from which this area will be developed.

A variety of topics are available for presentations, allowing authors flexibility.

Prof. Emer. Dr. Satoru Okamoto
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 management system
  • Distributed energy system
  • Renewable energy system
  • Energy storage
  • Demand-side management (DSM) or demand-side response (DSR)
  • Home energy management system (HEMS)
  • Building and energy management system (BEMS)
  • Smart energy management system
  • Smart building
  • Smart city
  • Energy efficiency enhancement
  • System performance improvement
  • Energy modeling and simulation
  • Internet of Things (IoT)
  • Information and communication technology (ICT)
  • Virtual reality (VR), augmented reality (AR), and mixed reality (MR)
  • Big data
  • 5G
  • Smart grid
  • Intelligent control
  • Artificial intelligence (AI)
  • Machine learning
  • Deep learning

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 3476 KiB  
Article
Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning
by Felix Gabriel, Johannes Bergers, Franziska Aschersleben and Klaus Dröder
Energies 2021, 14(11), 3185; https://doi.org/10.3390/en14113185 - 29 May 2021
Cited by 3 | Viewed by 2244
Abstract
Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters [...] Read more.
Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Management)
►▼ Show Figures

Figure 1

19 pages, 7238 KiB  
Article
Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
by Alma Rodríguez, Marco Pérez-Cisneros, Julio C. Rosas-Caro, Carolina Del-Valle-Soto, Jorge Gálvez and Erik Cuevas
Energies 2021, 14(11), 3019; https://doi.org/10.3390/en14113019 - 23 May 2021
Cited by 14 | Viewed by 2158
Abstract
Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an [...] Read more.
Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop