Hour-Ahead Price Based Energy Management Scheme for Industrial Facilities

Huang, Xuefei; Hong, Seung Ho; Li, Yuting

doi:10.1109/tii.2017.2711648

Cited by 80 publications

(34 citation statements)

References 27 publications

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“…IIoT / ICPS [372], [386], [392], [393], [366], [367], [375], [394], [395], [398] WSAN [369]- [371], [377], [396], [373], [374], [376], [378], [379], [381] NCS -Industrial Robots [384] Assembly Line [62], [364], [365], [383], [385], [387], [6], [388]- [391], [399] M2M Communication [368], [397] whole toward minimizing energy consumption is proposed in [384]. Dynamic low-power reconfiguration [364] and machine energy consumption minimization [365] are key objectives of novel assembly lines.…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

“…The significantly important role of data in this process is demonstrated in [383] where the collected data are shown to improve energy consumption awareness and allows the manufacturing energy management systems to make further analysis and to identify where to take actions in the manufacturing process in order to reduce the energy consumption. There have been several energy management and energy consumption optimization methods for the assembly line in the recent literature, with the most notable focusing on production control [385], forecasting models with neural networks [387], mobile service composition [388], real-time demand bidding [389], ontological modeling [390], process parameter modeling [391], machine energy consumption profiling [6], and concurrent energy data collection [399]. Methodologies and a models which reliably dimension energy scavenger properties to M2M communication requirements and network needs, allowing industries to optimize the adoption of that technologies while keeping technical risks low [368].…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

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Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…Thus, nine sets Y m are derived. A clustering algorithm provides a mapping of N → K, where K is the number of clusters and 1 ≤ K ≤ N. Each generated cluster has a centroid, which is the average of all patterns of the same cluster: (27) where N k denotes the number of patterns of X m that belongs to cluster C k . The set of clusters is denoted as C k = {c k , k = 1, .…”

Section: Load Profiling Fundamentalsmentioning

confidence: 99%

“…In FCM, the clusters' centroids are obtained by Equation (37). The difference with Equation (27) is the presence of fuzziness parameter q and the element u of the partition matrix. Equation (38) implies that the patterns are assigned to all clusters with a membership degree u.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

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An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

et al. 2017

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Abstract:Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets.

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Ant colony resource optimization for Industrial IoT and CPS

Ramesh

Munnangi

Rajeyyagari

et al. 2021

Int J of Intelligent Sys

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Internet‐of‐Things (IoT) enabled cyber‐physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time‐periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT‐MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT‐MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT‐MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature.

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Hour-Ahead Price Based Energy Management Scheme for Industrial Facilities

Cited by 80 publications

References 27 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Ant colony resource optimization for Industrial IoT and CPS

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