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Energies
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Smart Management Energy Systems in Industry 4.0
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Smart Management Energy Systems in Industry 4.0

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (15 May 2019) | Viewed by 20276

Special Issue Editors

Dr. Renato Ferrero

E-Mail Website
Guest Editor
Dipartimento di Automatica e Informatica, Politecnico di Torino, 10129 Torino, Italy
Interests: ubiquitous computing; wireless sensor networks; RFID systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mario Collotta

E-Mail Website
Guest Editor
Engineering and Architecture Faculty, Kore University of Enna, 94100 Enna, Italy
Interests: expert systems and networks in distributed systems; IoT; real-time and healthcare application
Special Issues, Collections and Topics in MDPI journals
Dr. María Victoria Bueno Delgado

E-Mail Website
Guest Editor
Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Interests: Industry 4.0; e-healthcare; SDN; NFV; AI; networks planning; optimization
Prof. Dr. Hsing-Chung Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Interests: blockchain network security; Internet of Things application engineering and security; applied cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In its origins, the term Industry 4.0 was associated with the computerization of manufacturing and with the diffusion of new network technologies in order to improve the communication paradigm. Today, the definition and implementation of Industry 4.0 include a number of trends, such as the Internet of Things (IoT), digital manufacturing and cyber-physical systems. Among these key elements, energy aware systems in factory automation are emerging as a challenging trend of Industry 4.0. Technological advancements in the ability to collect, transfer and analyze data by using smart energy aware systems are at the aim of this trend.

Smart solutions in order to limit the power consumption of manufacturing line aim at developing and integrating new technologies and methods into smart factories in order to rapidly adapt and respond to changes in the markets’ demands for high-quality products. In fact, smart energy aware systems in factories lie at the core of both Industry 4.0 and smart manufacturing.

A variety of recent advanced technologies and approaches play important roles, by exploiting innovative technologies and solutions and/or optimization methods. They allow higher levels of adaptively and flexibility in energy aware systems.

This Special Issue solicits high quality and unpublished work on recent advances in energy aware smart systems in Industry 4.0. The main topics of interest include, but are not limited to, the following:

  • Green Communication architectures and technologies for Industry 4.0;
  • Sustainable design and solutions for green automation systems for factory.
  • System on-chip vs network on-chip architectures in green automation systems for Industry 4.0;
  • Energy-efficient communications and management in medical automation application;
  • Big data and data management for industrial automation scenarios energy aware;
  • Security and cyber-security; test-bed, prototype, and practical systems for green communication in industrial automation use cases.

Dr. Renato Ferrero
Prof. Mario Collotta
Prof. Maria Victoria Bueno-Delgado
Prof. Hsing-Chung Chen
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 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. Energies 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 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

  • green communication
  • green automation systems, energy aware systems
  • factory automation

Published Papers (5 papers)

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Research

20 pages, 5677 KiB  
Article
Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction
by Jeng-Wei Lin, Shih-wei Liao and Fang-Yie Leu
Energies 2019, 12(13), 2523; https://doi.org/10.3390/en12132523 - 30 Jun 2019
Cited by 13 | Viewed by 2948
Abstract
Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product [...] Read more.
Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experimental results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power. Full article
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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13 pages, 2938 KiB  
Article
Real-Time Idle Time Cancellation by Means of Miniterm 4.0
by Eduardo Garcia and Nicolás Montés
Energies 2019, 12(7), 1230; https://doi.org/10.3390/en12071230 - 30 Mar 2019
Cited by 3 | Viewed by 2851
Abstract
The paper presents how single-model robotized manufacturing lines are rebalanced to save energy. The key idea is to eliminate idle time that each robot has by means of adjusting the velocity. To do so, the proposed technique predicts the idle time for the [...] Read more.
The paper presents how single-model robotized manufacturing lines are rebalanced to save energy. The key idea is to eliminate idle time that each robot has by means of adjusting the velocity. To do so, the proposed technique predicts the idle time for the next cycle time based on miniterm 4.0. This system measures in real-time the sub-cycle times (mini-terms) with the goal to detect disturbances that predict future machine failures. Mini-terms are used to compute the idle time and the allowed velocity reduction for the Industrial Robot without losing productivity. The proposed predictive control technique has been tested in a real production line located at Ford Almussafes plant (Valencia). The line has six stations where each one has an industrial robot. It is connected to miniterm 4.0 to perform a real test. A discussion, limitations of the technique, future implementations and conclusions are shown at the end of this paper. Full article
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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18 pages, 7723 KiB  
Article
Developing a BLE Beacon-Based Location System Using Location Fingerprint Positioning for Smart Home Power Management
by ChihKun Ke, MeiYu Wu, YuWei Chan and KeCheng Lu
Energies 2018, 11(12), 3464; https://doi.org/10.3390/en11123464 - 11 Dec 2018
Cited by 27 | Viewed by 6162
Abstract
In recent years, smart homes have begun to use various sensors to detect the location of users indoors. However, such sensors may not be stable, resulting in high detection error rates. Thus, how to improve indoor positioning accuracy has become an important topic. [...] Read more.
In recent years, smart homes have begun to use various sensors to detect the location of users indoors. However, such sensors may not be stable, resulting in high detection error rates. Thus, how to improve indoor positioning accuracy has become an important topic. This study explored Bluetooth Low Energy (BLE) Beacon indoor positioning for smart home power management. A novel system framework using BLE Beacon was proposed to detect the user location, and to perform power management in the home through a mobile device application. Since the BLE Beacon may produce a multipath effect, this study used the positioning algorithm and hardware configuration to reduce the error rate. Location fingerprint positioning algorithm and filter modification were used to establish a positioning method for facilitating deployment, and to reduce the required computing resources. The experiments included an observation of the Received Signal Strength Indicators (RSSI) and selecting filters and a discussion of the relationship between the characteristics of the BLE Beacon signal accuracy and the number of the BLE Beacons deployed in the observation space. The BLE Beacon multilateration positioning was combined with the In-Snergy intelligent energy management system for smart home power management. The contribution of this study is to allow users to enjoy smart home services based on their location within the home using a mobile device application. Full article
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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13 pages, 769 KiB  
Article
Robust Optimization of Power Consumption for Public Buildings Considering Forecasting Uncertainty of Environmental Factors
by Jingshu Xiao, Jun Xie, Xingying Chen, Kun Yu, Zhenyu Chen and Kaining Luan
Energies 2018, 11(11), 3075; https://doi.org/10.3390/en11113075 - 08 Nov 2018
Cited by 5 | Viewed by 2601
Abstract
In recent years, with the advancement of urban construction in China, the optimization of power consumption in public buildings has been focused on. The optimization of power consumption in public buildings is based on the prediction of natural illuminance, outdoor air temperature and [...] Read more.
In recent years, with the advancement of urban construction in China, the optimization of power consumption in public buildings has been focused on. The optimization of power consumption in public buildings is based on the prediction of natural illuminance, outdoor air temperature and flow of people in public building. Therefore, it is worthwhile to study how to formulate a power consumption strategy with consideration of forecasting uncertainty of environmental factors. The robust-index method is proposed to deal with the problem of forecasting uncertainty. Firstly, this paper establishes power consumption models for lighting systems, air-conditioning systems, and elevator systems in public buildings. Secondly, the robust indexes for each system and the synthetic robust index are established. Thirdly, the objective function is formulated to reduce the total electricity cost with the robust indexes applied as additional constraints to the optimization problem, therefore the obtained power consumption schedules are able to reach the expected robust level. Finally, simulation results show attributes of the proposed method. Full article
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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19 pages, 6875 KiB  
Article
A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach
by Tengfei Ma, Junyong Wu, Liangliang Hao, Huaguang Yan and Dezhi Li
Energies 2018, 11(10), 2858; https://doi.org/10.3390/en11102858 - 22 Oct 2018
Cited by 36 | Viewed by 4347
Abstract
This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to [...] Read more.
This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers. Full article
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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