Predictive analysis in energy management system

Hock, Keith P.; Radjabli, Kiamran; McGuiness, David; Boddeti, Murali

doi:10.1109/eeeic.2016.7555506

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…Active maintenance can tolerate interference events to a certain extent by extracting predictive knowledge from historical data. It can be divided into methods of semi-online methods [30,31], near-online methods [32], and forward-looking methods [33]. Reactive maintenance is defined by Kerr in the literature as "real-time and continuous adjustment of the schedule to keep it consistent with ongoing and unexpected events" [34].…”

Section: (C) Impact Evaluation Strategiesmentioning

confidence: 99%

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

Ren¹,

Xu²,

Wang³

et al. 2023

CMES

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The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server. The shop-leveled digital twin maintenance model is designed for production planning, which gives production instructions from the private industrial cloud server. To cope with the dynamic disturbances of a PMS, a big data-driven framework is proposed to control the three-level digital twin models, which contains indicator prediction, influence evaluation, and decisionmaking. Finally, a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs. The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system's respects.

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Section: (C) Impact Evaluation Strategiesmentioning

confidence: 99%

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

Ren¹,

Xu²,

Wang³

et al. 2023

CMES

View full text Add to dashboard Cite

show abstract

“…Agent‐based dynamic scheduling and control methods can significantly improve the flexibility and responsiveness of manufacturing systems [18–20]. Predictive scheduling algorithms can be used to analyse historical data on how to schedule resources to make the manufacturing system more resilient [21]. A flexible scheduling framework can be applied to optimise the manufacturing system by adjusting the processing order, system parameters, and structure [22].…”

Section: Resilience and Viability Of Shop Floorsmentioning

confidence: 99%

Address business crisis caused by COVID‐19 with collaborative intelligent manufacturing technologies

Shen

Yang

Gao

2020

IET Collaborative Intelligent Manufacturing

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“…It is required to address the modifications that are mandatory for electrical utilities to improve infrastructures and functions to surpass imminent challenges. These modifications include enhanced situational consciousness and visualizations, reliability, network automation, and security [24]. The foremost step of strategic energy management is to set out how energy will be managed by the organization.…”

Section: Energy Management Systemmentioning

confidence: 99%

“…Organization, policy, information system, awareness, and investment were the five classifications taken into account while evaluating the energy management measure matrix [24,26]. The considered categories are acquainted with other means of energy management practices.…”

Section: Energy Managementmentioning

confidence: 99%

Empirical Investigation of Barriers and Driving Forces for Efficient Energy Management Practices in Non-Energy-Intensive Manufacturing Industries of Bangladesh

Hasan¹,

Hossain²,

Tuhin

et al. 2019

Sustainability

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Improved energy efficiency is being considered as one of the significant challenges to mitigating climate change all over the world. While developed countries have already adopted energy management and auditing practices to improve energy efficiency, the developing countries lag far behind. There are a limited number of studies which have been conducted in the context of developing countries, which mostly revolve around highly energy-intensive sectors. This study looks into the existence and importance of the challenges to and motivating forces for the adoption of energy management practices in Bangladesh, a developing country, focusing on the non-energy-intensive manufacturing industries. Conducted as a multiple case study, the results indicate the existence of several barriers towards adopting and implementing the management of energy practices in the non-energy-intensive industries of Bangladesh, where among them, “other preferences for capital venture” and “inadequate capital expenditure” are the most dominant. This study also identified a number of driving forces that can accelerate the acceptance of energy efficiency practices, such as the demands from the owner, loans, subsidies, and a lowered cost–benefit ratio. Findings of this study could assist the concerned stakeholders to develop beneficial policies and a proper regulatory framework for the non-energy-intensive industries of developing countries like Bangladesh.

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Predictive analysis in energy management system

Cited by 8 publications

References 1 publication

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

Address business crisis caused by COVID‐19 with collaborative intelligent manufacturing technologies

Empirical Investigation of Barriers and Driving Forces for Efficient Energy Management Practices in Non-Energy-Intensive Manufacturing Industries of Bangladesh

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