Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Intelligent Control and Optimization in Energy System
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Control and Optimization in Energy System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 4776

Special Issue Editors

Dr. Dongjun Suh

E-Mail Website
Guest Editor
Department of Convergence & Fusion System Engineering, Kyungpook National University, Sangju, Republic of Korea
Interests: data science; AI; machine learning; smart control; energy ICT
Special Issues, Collections and Topics in MDPI journals
Dr. Hyunseok Kim

E-Mail Website
Guest Editor
Department of Computer Science, Dong-A University, Busan, Republic of Korea
Interests: reinforcement learning; robot; AI; swarm intelligence; embedded system

Special Issue Information

Dear Colleagues,

In the era of the Fourth Industrial Revolution, as the climate change crisis accelerates, Many researchers around the world are actively harnessing the potential of AI-based technologies to address the multifaceted challenges posed by climate change.

This Special Issue encompasses a wide range of technological development topics focused on the objectives of reducing energy consumption and optimizing energy generation. These efforts span various domains, including urban environments, industrial facilities, buildings, plant systems, surveillance and security, renewable energy systems, and various other large-scale infrastructures.

Within the domain of energy AI research, there is a deliberate emphasis on leveraging statistical methodologies to extract invaluable insights from extensive and intricate datasets that we have gathered. This strategic application of statistical expertise facilitates effective problem-solving and the gradual evolution of intelligent control technologies and optimization strategies. Additionally, an unwavering commitment to machine learning research propels the pursuit of practical applications across diverse energy systems, complemented by the advancement of intelligent control technologies.

We strongly encourage researchers from diverse fields within the journal's scope to contribute research papers that highlight the latest developments in their research area. Additionally, we invite researchers to collaborate with relevant experts and colleagues in preparing these contributions. Topics of interest for this Special Issue include, but are not limited to:

  • Energy AI and big data analysis;
  • Building energy efficiency;
  • Renewable energy systems;
  • Nuclear energy systems;
  • Optimization in energy management;
  • HVAC/Facilities control management;
  • Life cycle cost analysis in energy system;
  • Energy retrofit;
  • Smart systems in built environment;
  • Suveillance and security;
  • Fault diagnosis;
  • Interdisciplinary energy research.

Dr. Dongjun Suh
Dr. Hyunseok Kim
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 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. Applied Sciences 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 2400 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 AI
  • data science
  • smart systems
  • interdisciplinary study

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 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

15 pages, 2667 KiB  
Article
Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks
by Hyunjin Cho and Hyunseok Kim
Appl. Sci. 2025, 15(5), 2773; https://doi.org/10.3390/app15052773 - 4 Mar 2025
Viewed by 822
Abstract
This study proposes a novel approach to enhance the stability and performance of reinforcement learning (RL) in long-horizon tasks. Overestimation bias in value function estimation and high uncertainty within environments make it difficult to determine the optimal action. To address this, we improve [...] Read more.
This study proposes a novel approach to enhance the stability and performance of reinforcement learning (RL) in long-horizon tasks. Overestimation bias in value function estimation and high uncertainty within environments make it difficult to determine the optimal action. To address this, we improve the truncated quantile critics algorithm by managing uncertainty in robotic applications. Our dynamic method adjusts the discount factor based on policy entropy, allowing for fine-tuning that reflects the agent’s learning status. This enables the existing algorithm to learn stably even in scenarios with limited training data, ensuring more robust adaptation. By leveraging policy entropy loss, this approach effectively boosts confidence in predicting future rewards. Our experiments demonstrated an 11% increase in average evaluation return compared to traditional fixed-discount-factor approaches in the DeepMind Control Suite and Gymnasium robotics environments. This approach significantly enhances sample efficiency and adaptability in complex long-horizon tasks, highlighting the effectiveness of entropy-guided RL in navigating challenging and uncertain environments. Full article
(This article belongs to the Special Issue Intelligent Control and Optimization in Energy System)
►▼ Show Figures

Figure 1

21 pages, 2877 KiB  
Article
A Low-Cost IoT System Based on the ESP32 Microcontroller for Efficient Monitoring of a Pilot Anaerobic Biogas Reactor
by Sotirios D. Kalamaras, Maria-Athina Tsitsimpikou, Christos A. Tzenos, Antonios A. Lithourgidis, Dimitra S. Pitsikoglou and Thomas A. Kotsopoulos
Appl. Sci. 2025, 15(1), 34; https://doi.org/10.3390/app15010034 - 24 Dec 2024
Cited by 1 | Viewed by 2282
Abstract
A pilot anaerobic bioreactor requires near-daily monitoring and frequent maintenance. This study aimed to upgrade a pilot bioreactor into a low-cost IoT device via ESP32 microcontrollers. The methodology was based on remote data acquisition and online monitoring of various parameters towards assessing the [...] Read more.
A pilot anaerobic bioreactor requires near-daily monitoring and frequent maintenance. This study aimed to upgrade a pilot bioreactor into a low-cost IoT device via ESP32 microcontrollers. The methodology was based on remote data acquisition and online monitoring of various parameters towards assessing the anaerobic digestion performance. A semi-continuous tank bioreactor with a 60 L total volume was initially inoculated mainly with livestock manure and fed daily with a mixture of glucose, gelatin, and oleic acid, supplemented with a basic anaerobic medium. Under steady-state conditions, the organic loading rate was 2 g VS LR−1 d−1. Sensors for pH, temperature, REDOX potential, and ammonium concentration, along with devices measuring biogas volume and methane content, were integrated and validated against analytical methods. Biogas production was recorded accurately, enabling the early detection of production declines through ex-situ data analysis. Methane concentration variance was less than 6% compared to gas chromatography, while temperature and pH deviations were 0.15% and 1.67%, respectively. Ammonia ion measurements required frequent recalibration due to larger fluctuations. This IoT-enhanced system effectively demonstrated real-time monitoring of critical bioreactor parameters, with ESP32 enabling advanced control and monitoring capabilities. Full article
(This article belongs to the Special Issue Intelligent Control and Optimization in Energy System)
►▼ Show Figures

Figure 1

18 pages, 5559 KiB  
Article
Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building
by Junghoon Wee, Yeonghun Hong and Young Tae Chae
Appl. Sci. 2024, 14(21), 9889; https://doi.org/10.3390/app14219889 - 29 Oct 2024
Cited by 1 | Viewed by 887
Abstract
This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing [...] Read more.
This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort. Full article
(This article belongs to the Special Issue Intelligent Control and Optimization in Energy System)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop