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Electronics
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AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization
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AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 2368

Special Issue Editor

Dr. Xinlin Wang

E-Mail Website
Guest Editor
Energy Business Unit, Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT 2601, Australia
Interests: AI; AI-for-energy; sustainable development; Internet of Things (IoT); smart manufacturing

Special Issue Information

Dear Colleagues,

The advancement of smart energy systems is crucial for achieving energy efficiency, resilience, and sustainability. Artificial Intelligence (AI) has become a transformative tool, enabling significant improvements in the energy domain, such as enhancing prediction and detection accuracy and optimizing operations. This Special Issue aims to compile cutting-edge research on AI-empowered methods that enhance the functionality and intelligence of smart energy systems. We invite original research articles and reviews that explore AI applications in areas such as prediction, anomaly detection, demand response, smart buildings, HVAC control, demand-side management, distributed energy resource management, and more. By leveraging various learning algorithms, these studies aim to advance research in the energy sector comprehensively. Contributions that address challenges related to big data analytics, cybersecurity in energy systems, and the implementation of AI in smart grids are also welcome. This Special Issue seeks to foster innovation and collaboration by bringing together expertise from academia and industry, ultimately contributing to developing intelligent and sustainable energy infrastructures.

Dr. Xinlin Wang
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. Electronics 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 systems
  • artificial intelligence
  • machine learning
  • prediction
  • anomaly detection
  • demand response
  • distributed energy resource management
  • smart building
  • HVAC control
  • Internet of Things (IoT)

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Published Papers (3 papers)

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Research

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19 pages, 4090 KiB  
Article
Transmission Line Defect Detection Algorithm Based on Improved YOLOv12
by Yanpeng Ji, Tianxiang Ma, Hongliang Shen, Haiyan Feng, Zizi Zhang, Dan Li and Yuling He
Electronics 2025, 14(12), 2432; https://doi.org/10.3390/electronics14122432 - 14 Jun 2025
Cited by 1 | Viewed by 736
Abstract
To address the challenges of high missed detection rates for minute transmission line defects, strong complex background interference, and limited computational power on edge devices in UAV-assisted power line inspection, this paper proposes a lightweight improved YOLOv12 real-time detection model. First, a Bidirectional [...] Read more.
To address the challenges of high missed detection rates for minute transmission line defects, strong complex background interference, and limited computational power on edge devices in UAV-assisted power line inspection, this paper proposes a lightweight improved YOLOv12 real-time detection model. First, a Bidirectional Weighted Feature Fusion Network (BiFPN) is introduced to enhance bidirectional interaction between shallow localization information and deep semantic features through learnable feature layer weighting, thereby improving detection sensitivity for line defects. Second, a Cross-stage Channel-Position Collaborative Attention (CPCA) module is embedded in the BiFPN’s cross-stage connections, jointly modeling channel feature significance and spatial contextual relationships to effectively suppress complex background noise from vegetation occlusion and metal reflections while enhancing defect feature representation. Furthermore, the backbone network is reconstructed using ShuffleNetV2’s channel rearrangement and grouped convolution strategies to reduce model complexity. Experimental results demonstrate that the improved model achieved 98.7% mAP@0.5 on our custom transmission line defect dataset, representing a 3.0% improvement over the baseline YOLOv12, with parameters compressed to 2.31M (8.3% reduction) and real-time detection speed reaching 142.7 FPS. This method effectively balances detection accuracy and inference efficiency, providing reliable technical support for unmanned intelligent inspection of transmission lines. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization)
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19 pages, 3370 KiB  
Article
iTransformer-FFC: A Frequency-Aware Transformer Framework for Multi-Scale Time Series Forecasting
by Yongli Tang and Zhongqi Cai
Electronics 2025, 14(12), 2378; https://doi.org/10.3390/electronics14122378 - 10 Jun 2025
Viewed by 694
Abstract
Capturing complex temporal dependencies across multiple scales remains a fundamental challenge in time series forecasting. Transformer-based models have achieved impressive performance on sequence tasks, but vanilla designs often struggle to integrate information from both local fluctuations and global trends, especially in non-stationary sequences. [...] Read more.
Capturing complex temporal dependencies across multiple scales remains a fundamental challenge in time series forecasting. Transformer-based models have achieved impressive performance on sequence tasks, but vanilla designs often struggle to integrate information from both local fluctuations and global trends, especially in non-stationary sequences. We propose iTransformer-FFC, a novel forecasting framework that addresses these issues through frequency-domain analysis and multi-scale feature fusion. In particular, iTransformer-FFC introduces a Fast Fourier Convolution (FFC) module to transform time series data into the frequency domain, isolating dominant periodic components and attenuating noise before attention is applied. A hierarchical feature fusion mechanism that integrates features extracted at multiple temporal resolutions then jointly models global and local temporal patterns, while a factorized self-attention architecture reduces the quadratic complexity of standard Transformers, improving efficiency while maintaining accuracy. Together, these innovations enable more effective long-range dependency modeling and adaptability to regime shifts in the data. Extensive experiments on five public benchmark datasets demonstrate that iTransformer-FFC consistently outperforms state-of-the-art models, including the vanilla Transformer, an earlier iTransformer variant, and PatchTST. Notably, our model achieves on average an 8.73% lower MSE and 6.95% lower MAE than the best performing baseline, confirming its superior predictive accuracy and generalization in multi-scale time series forecasting through its innovative integration of frequency-domain analysis, hierarchical feature fusion, and factorized attention mechanisms. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization)
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Review

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20 pages, 601 KiB  
Review
Neural Moving Horizon Estimation: A Systematic Literature Review
by Surrayya Mobeen, Jann Cristobal, Shashank Singoji, Basaam Rassas, Mohammadreza Izadi, Zeinab Shayan, Amin Yazdanshenas, Harneet Kaur Sohi, Robert Barnsley, Lana Elliott and Reza Faieghi
Electronics 2025, 14(10), 1954; https://doi.org/10.3390/electronics14101954 - 11 May 2025
Cited by 1 | Viewed by 507
Abstract
The neural moving horizon estimator (NMHE) is a relatively new and powerful state estimator that combines the strengths of neural networks (NNs) and model-based state estimation techniques. Various approaches exist for constructing NMHEs, each with its unique advantages and limitations. However, a comprehensive [...] Read more.
The neural moving horizon estimator (NMHE) is a relatively new and powerful state estimator that combines the strengths of neural networks (NNs) and model-based state estimation techniques. Various approaches exist for constructing NMHEs, each with its unique advantages and limitations. However, a comprehensive literature review that consolidates existing knowledge, outlines design guidelines, and highlights future research directions is currently lacking. To address this gap, this systematic review screened 1164 records and ultimately included 22 primary studies, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. This paper (1) explains the fundamental principles of NMHEs, (2) explores three major NMHE architectures, (3) analyzes the types of NNs used, such as multi-layer perceptrons (MLPs), long short-term memory networks (LSTMs), radial basis function networks (RBFs), and fuzzy neural networks, (4) reviews real-time implementability—including reported execution times ranging from 1.6 μs to 11.28 s on different computing hardware—and (5) identifies common limitations and future research directions. The findings show that NMHEs can be realized in three principal ways: model learning, cost function learning, and approximating the real-time optimization in moving horizon estimation. Cost function learning offers flexibility in capturing task-specific estimation goals, while model learning and optimization approximation approaches tend to improve estimation accuracy and computational speed, respectively. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization)
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