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JLPEA
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Energy Consumption Management in Electronic Systems
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Energy Consumption Management in Electronic Systems

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: 30 October 2026 | Viewed by 3688

Special Issue Editors

Prof. Dr. Spyridon Nikolaidis

E-Mail Website
Guest Editor
Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: digital design; low-power design techniques; applications development on embedded systems; sound-based pipe leak detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ronald Tetzlaff

E-Mail Website
Guest Editor
Institute of Circuits and Systems, TUD, Dresden University of Technology, 01062 Dresden, Germany
Interests: circuit theory; memristors; chaotic circuits; nonlinear dynamics; AI; machine learning; cellular neural networks; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy consumption in electronic systems is one of the most critical factors for the sustainability, performance, and cost-effectiveness of modern technological applications. From portable devices and embedded systems to large-scale infrastructures, optimizing energy management is essential for achieving high efficiency and reducing environmental impact. Energy consumption management comes with significant challenges, such as improving microprocessor efficiency, managing energy in distributed systems, and implementing power-saving strategies in artificial intelligence and edge computing applications. Current trends focus on dynamic power adaptation methods, advanced energy management algorithms, and innovative energy harvesting techniques. This Special Issue aims to highlight innovative methods, technologies, and strategies that contribute to improving the energy efficiency of electronic systems. We welcome studies that will enrich our knowledge and provide new tools and perspectives for the academic and industrial community.

Topics of the Special Issue

This Special Issue garners pioneering research that focuses on the key aspects of energy management in electronic systems, covering the following topics:

  • Energy reduction in embedded and IoT systems: Strategies for minimizing power consumption in portable and networked devices.
  • Energy management algorithms: Real-time mechanisms for adapting power consumption.
  • Low-power architectures: Hardware and software design aimed at efficient energy use.
  • Energy harvesting mechanisms: The utilization of energy harvesting techniques and renewable energy sources.
  • Energy efficiency in data centers and cloud infrastructures: The optimization of energy usage in large-scale information systems.
  • Machine learning and AI for energy management: Intelligent approaches for predictive analysis and adaptive consumption.

Prof. Dr. Spyridon Nikolaidis
Prof. Dr. Ronald Tetzlaff
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. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly 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 1800 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.

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

Published Papers (3 papers)

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Research

32 pages, 8214 KB  
Article
Static Voltage Stability Assessment of Renewable Energy Power Systems Based on DBN-LSTM Power Forecasting
by Qiang Wang, Libo Yang, Mengdi Wang, Bin Ma, Long Yuan, Shaobo Li and Zhangjie Liu
J. Low Power Electron. Appl. 2026, 16(2), 11; https://doi.org/10.3390/jlpea16020011 - 24 Mar 2026
Cited by 1 | Viewed by 578
Abstract
High penetration of renewable energy sources (RESs) introduces significant power fluctuations, threatening voltage and frequency stability in modern power systems. This paper presents an integrated framework for static voltage stability assessment and stability-constrained optimization of under-frequency load shedding (UFLS) in renewable-dominated grids. A [...] Read more.
High penetration of renewable energy sources (RESs) introduces significant power fluctuations, threatening voltage and frequency stability in modern power systems. This paper presents an integrated framework for static voltage stability assessment and stability-constrained optimization of under-frequency load shedding (UFLS) in renewable-dominated grids. A low-conservativeness analytical criterion is first derived for static voltage stability margin assessment. Then, a hybrid Deep Belief Network–Long Short-Term Memory (DBN–LSTM) model is developed for accurate renewable power forecasting, capturing temporal variability and uncertainty. Finally, UFLS-based stability-constrained dispatch is formulated to prevent voltage collapse, enhance the system stability, and minimize RES curtailment. Simulations on a modified IEEE benchmark system demonstrate that the proposed approach improves voltage and frequency stability while maintaining high renewable energy utilization. Full article
(This article belongs to the Special Issue Energy Consumption Management in Electronic Systems)
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19 pages, 1057 KB  
Article
Efficient Energy Consumption: Leveraging AI Models for Appliance Detection
by Gerardo Arno Sonck-Martinez, Victor A. Gonzalez-Huitron, Abraham Efraím Rodríguez-Mata, Isidro Robledo-Vega, Guillermo Valencia-Palomo and Jose-Agustin Almaraz-Damian
J. Low Power Electron. Appl. 2026, 16(1), 9; https://doi.org/10.3390/jlpea16010009 - 25 Feb 2026
Viewed by 841
Abstract
This research addresses the increasing need for efficient energy management in residential settings in response to the increasing global energy demands, focusing on the integration of artificial intelligence to identify energy burdens. We employ and compare some machine learning models, like Decision Trees, [...] Read more.
This research addresses the increasing need for efficient energy management in residential settings in response to the increasing global energy demands, focusing on the integration of artificial intelligence to identify energy burdens. We employ and compare some machine learning models, like Decision Trees, K-nearest neighbors, and Feedforward Neural Networks, with a primary focus on electrical current as a key parameter. The Fine K-NN model shows notable efficiency, achieving an accuracy of 99.1% in the identification of active household appliances using a single sensor. Our methodology encompasses rigorous data acquisition and preprocessing under controlled experimental conditions, ensuring the integrity and reliability of our results. This study contributes to the field by illustrating the effectiveness of specific AI models in energy management under controlled conditions, paving the way for future advancements in AI-driven energy conservation strategies. Full article
(This article belongs to the Special Issue Energy Consumption Management in Electronic Systems)
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16 pages, 955 KB  
Article
A Multiplierless Architecture for Image Convolution in Memory
by John Reuben, Felix Zeller, Benjamin Seiler and Dietmar Fey
J. Low Power Electron. Appl. 2025, 15(4), 63; https://doi.org/10.3390/jlpea15040063 - 23 Oct 2025
Viewed by 1350
Abstract
Image convolution is a commonly required task in machine vision and Convolution Neural Networks (CNNs). Due to the large data movement required, image convolution can benefit greatly from in-memory computing. However, image convolution is very computationally intensive, requiring [...] Read more.
Image convolution is a commonly required task in machine vision and Convolution Neural Networks (CNNs). Due to the large data movement required, image convolution can benefit greatly from in-memory computing. However, image convolution is very computationally intensive, requiring (n−(k−1))2 Inner Product (IP) computations for convolution of a n×n image with a k×k kernel. For example, for a convolution of a 224 × 224 image with a 3 × 3 kernel, 49,284 IPs need to be computed, where each IP requires nine multiplications and eight additions. This is a major hurdle for in-memory implementation because in-memory adders and multipliers are extremely slow compared to CMOS multipliers. In this work, we revive an old technique called ‘Distributed Arithmetic’ and judiciously apply it to perform image convolution in memory without area-intensive hard-wired multipliers. Distributed arithmetic performs multiplication using shift-and-add operations, and they are implemented using CMOS circuits in the periphery of ReRAM memory. Compared to Google’s TPU, our in-memory architecture requires 56× less energy while incurring 24× more latency for convolution of a 224 × 224 image with a 3 × 3 filter. Full article
(This article belongs to the Special Issue Energy Consumption Management in Electronic Systems)
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