Energy Load Profile Analysis on Machine Level

Hanno Teiwes; Stefan Blume; Christoph Herrmann; Markus Rössinger; Sebastian Thiede

doi:10.1016/j.procir.2017.11.073

Energy Load Profile Analysis on Machine Level

Hanno Teiwes^*, Stefan Blume, Christoph Herrmann, Markus Rössinger, Sebastian Thiede

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › Academic › peer-review

45 Citations (Scopus)

627 Downloads (Pure)

Abstract

The metering and analysis of energy demands is widely applied in today's manufacturing industry in order to reduce energy costs and environmental impacts alike. However, especially on machine level the interpretation of energy data is still challenging due to huge amounts of metering data and a lack of methodological knowledge. The presented approach focuses on the evaluation of electric load profiles on machine and workgroup level in dependency on available complementing data such as product, scheduling or machine data. The approach aims at extracting a maximum degree of information from the available data in order to improve machine operation modes, production scheduling, energy cost allocation and factory planning processes. The approach is exemplified by use cases from the automotive industry.

Original language	English
Pages (from-to)	271-276
Number of pages	6
Journal	Procedia CIRP
Volume	69
DOIs	https://doi.org/10.1016/j.procir.2017.11.073
Publication status	Published - 1 Jan 2018
Externally published	Yes
Event	25th CIRP Conference on Life Cycle Engineering, LCE 2018 - Copenhagen, Denmark Duration: 30 Apr 2018 → 2 May 2018 Conference number: 25

Keywords

Energy demand
Load profile analysis
Planning phase

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.procir.2017.11.073Licence: CC BY-NC-ND

Teiwes2018energyFinal published version, 1.02 MBLicence: CC BY-NC-ND

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title = "Energy Load Profile Analysis on Machine Level",

abstract = "The metering and analysis of energy demands is widely applied in today's manufacturing industry in order to reduce energy costs and environmental impacts alike. However, especially on machine level the interpretation of energy data is still challenging due to huge amounts of metering data and a lack of methodological knowledge. The presented approach focuses on the evaluation of electric load profiles on machine and workgroup level in dependency on available complementing data such as product, scheduling or machine data. The approach aims at extracting a maximum degree of information from the available data in order to improve machine operation modes, production scheduling, energy cost allocation and factory planning processes. The approach is exemplified by use cases from the automotive industry.",

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author = "Hanno Teiwes and Stefan Blume and Christoph Herrmann and Markus R{\"o}ssinger and Sebastian Thiede",

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doi = "10.1016/j.procir.2017.11.073",

language = "English",

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TY - JOUR

T1 - Energy Load Profile Analysis on Machine Level

AU - Teiwes, Hanno

AU - Blume, Stefan

AU - Herrmann, Christoph

AU - Rössinger, Markus

AU - Thiede, Sebastian

N1 - Conference code: 25

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The metering and analysis of energy demands is widely applied in today's manufacturing industry in order to reduce energy costs and environmental impacts alike. However, especially on machine level the interpretation of energy data is still challenging due to huge amounts of metering data and a lack of methodological knowledge. The presented approach focuses on the evaluation of electric load profiles on machine and workgroup level in dependency on available complementing data such as product, scheduling or machine data. The approach aims at extracting a maximum degree of information from the available data in order to improve machine operation modes, production scheduling, energy cost allocation and factory planning processes. The approach is exemplified by use cases from the automotive industry.

AB - The metering and analysis of energy demands is widely applied in today's manufacturing industry in order to reduce energy costs and environmental impacts alike. However, especially on machine level the interpretation of energy data is still challenging due to huge amounts of metering data and a lack of methodological knowledge. The presented approach focuses on the evaluation of electric load profiles on machine and workgroup level in dependency on available complementing data such as product, scheduling or machine data. The approach aims at extracting a maximum degree of information from the available data in order to improve machine operation modes, production scheduling, energy cost allocation and factory planning processes. The approach is exemplified by use cases from the automotive industry.

KW - Energy demand

KW - Load profile analysis

KW - Planning phase

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U2 - 10.1016/j.procir.2017.11.073

DO - 10.1016/j.procir.2017.11.073

M3 - Conference article

AN - SCOPUS:85047087560

SN - 2212-8271

VL - 69

SP - 271

EP - 276

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 25th CIRP Conference on Life Cycle Engineering, LCE 2018

Y2 - 30 April 2018 through 2 May 2018

ER -

Energy Load Profile Analysis on Machine Level

Abstract

Keywords

UN SDGs

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