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Sensor Application for Smart and Sustainable Energy Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 3001

Special Issue Editors


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Guest Editor
Electronics Department, University of Alcala, 28871 Madrid, Spain
Interests: smart sensors; FPGAs; embedded design; WSN; data mining
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electronics, University of Alcala, 28871 Madrid, Spain
Interests: sensors; detectors characterization; digital embedded systems; electronic design; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Meeting the EU's climate change and energy policy targets for 2050 will require a major transformation of our current electricity infrastructure. Energy proposals seek to adapt novel supply solutions to the energy consumer and suppliers’ needs. Under this context, smart energy philosophy turns the traditional approach to energy production and consumption into the efficient maximization of processes. The development of smart energy management systems establishes strategies combining digitalization and automation with energy administration, allowing decisions to be made regarding energy use. To this end, it is crucial to develop effective technological tools that collect and analyze data into meaningful information, enabling decisions to be made based on optimizing energy use, and establishing synergies among energy stakeholders. The acceptance of the application of novel technologies based on energy management will allow us to manage energy by reducing costs and enhancing long-term sustainability. Sensors play a vital role here, as they provide real-time data and information that enable the more efficient and effective control of energy resources. The aim of this Special Issue is to present innovative sensor applications in smart energy systems. Research articles that address new perspectives and experiences that analyze lessons learned in practice are highly welcome. Articles may include, but are not limited to, the following types of sensor applications:

  • Energy consumption monitoring for tracking the demand pattern of loads usage.
  • Occupancy sensors for energy waste reduction.
  • Temperature and humidity sensor monitoring to maintain comfort while minimizing energy consumption.
  • Lighting sensors to measure ambient light levels or adjust artificial lighting accordingly.
  • Power quality sensors to ensure a stable and efficient power supply.
  • Solar and wind sensors to monitor environmental conditions.
  • Energy storage sensors to ensure safe and efficient energy.
  • Gas and water flow sensors to manage energy-intensive processes and detect leaks or inefficiencies.
  • Thermal sensors to prevent equipment failures and reduce energy losses.
  • CO2 and air quality sensors to optimize ventilation systems.
  • Voltage and current sensors for the control and optimization of electrical loads.
  • Sub-metering sensors to identify and optimize energy-saving opportunities and allocate energy costs accurately.
  • Environmental sensors to inform energy management systems about weather conditions.
  • Security sensors to activate lighting, heating, or cooling systems for security purposes.

Prof. Dr. Ignacio Bravo
Dr. Carlos Cruz
Guest Editors

Manuscript Submission Information

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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

  • energy management system
  • sustainable development
  • smart technologies
  • smart energy and optimization
  • renewable energy sources
  • energy efficiency
  • smart sensors
  • sensor data analysis
  • IoT-based sensor technology

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Published Papers (1 paper)

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Research

30 pages, 4418 KiB  
Article
Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools
by Sergio Aquino-Brítez, Pablo García-Sánchez, Andrés Ortiz and Diego Aquino-Brítez
Sensors 2025, 25(3), 846; https://doi.org/10.3390/s25030846 - 30 Jan 2025
Viewed by 2244
Abstract
The growing global demand for computational resources, particularly in Artificial Intelligence (AI) applications, raises increasing concerns about energy consumption and its environmental impact. This study introduces a newly developed energy consumption index that evaluates the energy efficiency of Deep Learning (DL) models, providing [...] Read more.
The growing global demand for computational resources, particularly in Artificial Intelligence (AI) applications, raises increasing concerns about energy consumption and its environmental impact. This study introduces a newly developed energy consumption index that evaluates the energy efficiency of Deep Learning (DL) models, providing a standardized and adaptable approach for various models. Convolutional neural networks, including both classical and modern architectures, serve as the primary case study to demonstrate the applicability of the index. Furthermore, the inclusion of the Swin Transformer, a state-of-the-art and modern non-convolutional model, highlights the adaptability of the framework to diverse architectural paradigms. This study analyzes the energy consumption during both training and inference of representative DL architectures, including AlexNet, ResNet18, VGG16, EfficientNet-B3, ConvNeXt-T, and Swin Transformer, trained on the Imagenette dataset using TITAN XP and GTX 1080 GPUs. Energy measurements are obtained using sensor-based tools, including OpenZmeter (v2) with integrated electrical sensors. Additionally, software-based tools such as CarbonTracker (v1.2.5) and CodeCarbon (v2.4.1) retrieve energy consumption data from computational component sensors. The results reveal significant differences in energy efficiency across architectures and GPUs, providing insights into the trade-offs between model performance and energy use. By offering a flexible framework for comparing energy efficiency across DL models, this study advances sustainability in AI systems, supporting accurate and standardized energy evaluations applicable to various computational settings. Full article
(This article belongs to the Special Issue Sensor Application for Smart and Sustainable Energy Management)
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