Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.7
Journals
Entropy
Special Issues
Entropy Theory in Energy and Power Systems
entropy-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Entropy Theory in Energy and Power Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 901

Special Issue Editor

Dr. Min Xie

E-Mail Website
Guest Editor
School of Electric Power, South China University of Technology, Guangzhou 510640, China
Interests: intelligent scheduling and control of power systems; optimal operation and energy management of integrated energy systems; optimal decision making in power markets

Special Issue Information

Dear Colleagues,

Digital transformation is the contemporary direction of the energy and power industry. If society continues to rely solely on the traditional scheduling model based on energy and grid physics, combined with the actual scheduling experience of grid workers, it will be difficult to adapt to the complex and large-scale new power systems. Therefore, it will be essential to develop and research targeted data analysis techniques and advance their application in energy and power systems. Entropy provides an effective foundational theory for the planning, operation, and regulation of digital energy and power systems. Mining the potentially valuable spatiotemporal information in the massive data of the power system will completely change the model and the operational mode of traditional energy and power systems.

This Special Issue aims to provide a research and discussion platform for the application of entropy and its improved theories in the fields of energy and power systems. The scope of submissions includes, but is not limited to:

  1. New energy generation forecasting and load forecasting methods considering entropy theory.
  2. The application of entropy theory in the optimization of integrated energy systems and power systems.
  3. The application of entropy theory in fault diagnosis and analysis of integrated energy systems and power systems.
  4. Consider the application of entropy based comprehensive energy system, power system resilience modeling, and enhancement techniques.
  5. The application of physical statistical theory in stability and transient analysis of power systems.

Dr. Min Xie
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. Entropy is an international peer-reviewed open access monthly 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

  • entropy theory
  • new energy generation forecasting
  • load forecasting
  • optimization of integrated energy systems and power systems
  • fault diagnosis
  • resilience modeling

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

21 pages, 4909 KiB  
Article
Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions
by Min Xie, Shengzhen Lin, Kaiyuan Dong and Shiping Zhang
Entropy 2023, 25(9), 1343; https://doi.org/10.3390/e25091343 - 16 Sep 2023
Viewed by 784
Abstract
To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other [...] Read more.
To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system. Full article
(This article belongs to the Special Issue Entropy Theory in Energy and Power Systems)
►▼ 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