Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.2
Journals
Energies
Special Issues
Intelligent Phase Change Control and Thermal Management for Energy Applications:...
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Phase Change Control and Thermal Management for Energy Applications: 2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".

Deadline for manuscript submissions: 30 January 2026 | Viewed by 453

Special Issue Editors

Prof. Dr. Wenxiao Chu

E-Mail Website
Guest Editor
Key Laboratory of Thermo-Fluid Science and Engineering (Ministry of Education), Xi’an Jiaotong University, Xi’an 710049, China
Interests: energy systems; enhanced heat transfer; thermal management; heating, ventilating, and air conditioning (HVAC); supercritical fluid
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chi-Chuan Wang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
Interests: heat transfer; HVAC; energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intelligent phase change control and thermal management of energy applications has become a popular area of research in fields such as electronics, renewable energy systems, and energy storage. These technologies play a significant role in optimizing the efficiency, reliability, and lifespan of various energy devices.

This Special Issue aims to present and disseminate recent research regarding advanced theories, mechanisms, designs, models, applications and the control of AI thermal management technologies.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Cooling systems;
  • Refrigeration and air conditioning;
  • Dynamic cooling;
  • Phase change control;
  • Thermal management;
  • Optimized energy conversion;
  • Predictive maintenance;
  • Intelligence and automation;
  • Renewable energy;
  • AI application for electric vehicles;
  • Thermal management in data centers;
  • Energy saving in buildings and HVAC systems;
  • Heat and mass transfer enhancement;
  • CFD simulation and prediction;
  • Artificial intelligence application.

Prof. Dr. Wenxiao Chu
Prof. Dr. Chi-Chuan Wang
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. 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

  • cooling systems
  • refrigeration and air conditioning
  • dynamic cooling
  • phase change control
  • thermal management

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.

Related Special Issue

Published Papers (1 paper)

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

Research

16 pages, 325 KB  
Article
Electricity Demand Forecasting and Risk Assessment for Campus Energy Management
by Yon-Hon Tsai and Ming-Tang Tsai
Energies 2025, 18(20), 5521; https://doi.org/10.3390/en18205521 - 20 Oct 2025
Viewed by 259
Abstract
This paper employs the Grey–Markov Model (GMM) to predict users’ electricity demand and introduces the Enhanced Monte Carlo (EMC) method to assess the reliability of the prediction results. The GMM integrates the advantages of the Grey Model (GM) and the Markov Chain to [...] Read more.
This paper employs the Grey–Markov Model (GMM) to predict users’ electricity demand and introduces the Enhanced Monte Carlo (EMC) method to assess the reliability of the prediction results. The GMM integrates the advantages of the Grey Model (GM) and the Markov Chain to enhance prediction accuracy, while the EMC combines the Monte Carlo simulation with a dual-variable approach to conduct a comprehensive risk assessment. This framework helps decision-makers better understand electricity demand patterns and effectively manage associated risks. A university campus in southern Taiwan is selected as the case study. Historical data of monthly maximum electricity demand, including peak, semi-peak, Saturday semi-peak, and off-peak periods, were collected and organized into a database using Excel. The GMM was applied to predict the monthly maximum electricity demand for the target year, and its prediction results were compared with those obtained from the GM and Grey Differential Equation (GDE) models. The results show that the average Mean Absolute Percentage Error (MAPE) values for the GM, GDE, and GMM are 10.96341%, 9.333164%, and 6.56026%, respectively. Among the three models, the GMM exhibits the lowest average MAPE, indicating superior prediction performance. The proposed GMM demonstrates robust predictive capability and significant practical value, offering a more effective forecasting tool than the GM and GDE models. Furthermore, the EMC method is utilized to evaluate the reliability of the risk assessment. The findings of this study provide decision-makers with a reliable reference for electricity demand forecasting and risk management, thereby supporting more effective contract capacity planning. Full article
(This article belongs to the Special Issue Intelligent Phase Change Control and Thermal Management for Energy Applications: 2nd Edition)
►▼ Show Figures

Figure 1

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