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Symmetry
Special Issues
Applications in Symmetry/Asymmetry and Machine Learning
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Applications in Symmetry/Asymmetry and Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 299

Special Issue Editors

Dr. Tamara Zivkovic

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11000, Serbia
Interests: machine learning; software testing; metaheuristic optimization; explainable AI; deep learning
Dr. Jingang Shi

E-Mail Website
Guest Editor
School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, ‘Applications in Symmetry/Asymmetry and Machine Learning’. Machine learning and artificial intelligence have become central to solving complex problems across science, industry, and society. Symmetry, in this context, refers to balanced and recurring structures in data and models, such as repetitive patterns in time series or uniform distributions, while asymmetry appears in challenges such as class imbalance, unequal feature importance, or anomaly detection.

This Special Issue aims to highlight cutting-edge research that integrates concepts of symmetry and asymmetry into the development of machine learning models, optimization algorithms, and decision-making systems. Of particular interest are methods that achieve balance between exploration and exploitation in optimization, uncover hidden asymmetries in predictive modeling, and design explainable AI techniques that make algorithmic decisions more transparent.

Potential topics include, but are not limited to, supervised and unsupervised learning, time series analysis and prediction, metaheuristic and evolutionary optimization, deep learning and recurrent neural networks, anomaly detection, image and pattern recognition, decision support systems, and applications of explainable AI. Both original research and review articles are welcome.

We look forward to your contributions.

Dr. Tamara Zivkovic
Dr. Jingang Shi
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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry 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 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

  • machine learning
  • artificial intelligence
  • symmetry and asymmetry
  • metaheuristic optimization
  • deep learning
  • recurrent neural networks
  • explainable AI
  • time series prediction
  • pattern recognition
  • data analysis

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

Published Papers (1 paper)

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Research

28 pages, 3577 KB  
Article
Comparative Deep Learning Models for Short-Term Wind Power Forecasting: A Real-World Case Study from Tokat Wind Farm, Türkiye
by Avşin Ay, Kevser Önal, Ahmet Top, Cem Haydaroğlu, Heybet Kılıç and Özal Yıldırım
Symmetry 2026, 18(1), 11; https://doi.org/10.3390/sym18010011 (registering DOI) - 19 Dec 2025
Abstract
Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability [...] Read more.
Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability to capture or adapt to the underlying symmetries and asymmetries inherent in real-world wind energy data remains insufficiently explored. In this study, we evaluate and compare these models using authentic production and meteorological data from the Tokat Wind Farm in Türkiye. The forecasting scenarios were designed to reflect the temporal structure of the dataset, including seasonal patterns, recurrent behaviors, and the symmetry-breaking effects caused by abrupt changes in wind speed and operational variability. The results demonstrate that the LSTM model most effectively captures the temporal relationships and partial symmetries within the data, yielding the lowest error metrics (RMSE = 0.2355, MAE = 0.1249, MAPE = 25.16%, R2 = 0.8199). GRU and CNN offer moderate performance but show reduced sensitivity to asymmetric fluctuations, particularly during periods of high variability. The comparative findings highlight how symmetry-informed model behavior—specifically the ability to learn repeating temporal structures and respond to symmetry-breaking events—can significantly influence forecasting accuracy. This study provides practical insights into the interplay between data symmetries and model performance, supporting the development of more robust deep learning approaches for real-world wind energy forecasting. Full article
(This article belongs to the Special Issue Applications in Symmetry/Asymmetry and Machine Learning)
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