Decision-Guided Self-Architecting Framework for integrated distribution and Energy Management

Arinez, Jorge; Biller, Stephan; Brodsky, Alexander; Menascé, Daniel A.; Shao, Guodong; Sousa, Jorge Pinho de

doi:10.1109/isgt.2011.5759190

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…It removes guesswork and helps users to discover which efficiency opportunities offer the quickest or highest payback potential. With the appropriate abstract model views, the DGMS will support decision optimization functions such as [14]:…”

Section: Decision Guidance Management System and Dgqlmentioning

confidence: 99%

A Decision-Guided Energy Management Framework for Sustainable Manufacturing

Shao

Brodsky

Arinez

et al. 2011

Volume 2: 31st Computers and Information in Engineering Conference, Parts a and B

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A growing number of manufacturing industries are initiating efforts to address sustainability issues. According to the National Association of Manufacturers, the manufacturing sector currently accounts for about one third of all energy consumed in the United States [1]. Reducing energy costs and pollution emissions involves many areas within an industrial facility. Peak electric demands are a significant component in the cost of electricity. Electric demand management relates to electric tariff rates, new power generation, and incentives to curtail peak usages. Shifting some equipment/machine usage to the periods of lower cost or using stand-by local generators during the peak demand period can yield important savings. Analysis of these options is important to decision makers to avoid unnecessary high cost of energy and equipment. This paper proposes a Decision-Guided energy management in manufacturing (DG-EMM) framework to perform what-if analysis and make optimal actionable recommendations for a manufacturing facility both on (1) operational energy management including load shedding, curtailment, and local generation and (2) planning and investment decisions for introducing renewable technologies. The DG-EMM is based on the novel technology of the Decision-Guidance Query Language (DGQL), which is a tool for fast development and iterative extension of decision-guidance and optimization solutions. The proposed DG-EMM will support user-defined objectives for optimal recommendations, such as minimizing emissions and energy costs and maximizing Return on Investment (ROI). A case study of the peak demand control for an example manufacturing facility is discussed.

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Section: Decision Guidance Management System and Dgqlmentioning

confidence: 99%

A Decision-Guided Energy Management Framework for Sustainable Manufacturing

Shao

Brodsky

Arinez

et al. 2011

Volume 2: 31st Computers and Information in Engineering Conference, Parts a and B

Self Cite

View full text Add to dashboard Cite

show abstract

“…A Decision Guidance Management System (DGMS) was defined as a productivity platform for closed-loop data acquisition, learning, prediction and decision optimization [4]. The authors reported positive outcomes on the application of DGMS to supply chain management, energy distribution and management [4][5].…”

Section: Introductionmentioning

confidence: 96%

Multivariate Data-Driven Decision Guidance for clinical scientists

Burstein

Silva

Jelinek

et al. 2013

2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)

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Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management.The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.

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Decision-Guided Self-Architecting Framework for integrated distribution and Energy Management

Cited by 2 publications

References 21 publications

A Decision-Guided Energy Management Framework for Sustainable Manufacturing

A Decision-Guided Energy Management Framework for Sustainable Manufacturing

Multivariate Data-Driven Decision Guidance for clinical scientists

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