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Analysis techniques to estimate the overhead energy for industrial facilities and case studies

Analysis techniques to estimate the overhead energy for industrial facilities and case studies In this study, four important techniques for estimating the non-process energy (heating, cooling, lighting and ventilation) in industrial and manufacturing buildings were examined. The building energy data for two industrial facilities were collected and analysed. The building non-process energy includes lighting, heating, cooling and ventilation. The power intensity (kW/m2) for each energy type use was estimated using two or more methods (direct measurement of non-process energy, direct measurement of process energy, regression and simulation) and then analysed. The results of these estimates were close to each other which indicate that any method can be used based on available information. This non-process energy needs to be clearly defined to allow more quantitative improvements. The utilization of percentage of non-process energy in industrial and manufacturing plants is an important potential source of energy and cost improvements. Previous analysis of industrial energy use often expressed non-process energy as a percentage of total energy, but without clear values of actual non-process energy. This information is a low value since the actual non-process energy is then dominated by the dominator which is unspecified. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Analysis techniques to estimate the overhead energy for industrial facilities and case studies

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Analysis techniques to estimate the overhead energy for industrial facilities and case studies

Abstract

In this study, four important techniques for estimating the non-process energy (heating, cooling, lighting and ventilation) in industrial and manufacturing buildings were examined. The building energy data for two industrial facilities were collected and analysed. The building non-process energy includes lighting, heating, cooling and ventilation. The power intensity (kW/m2) for each energy type use was estimated using two or more methods (direct measurement of non-process energy, direct...
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Publisher
Taylor & Francis
Copyright
© 2015 Taylor & Francis
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2015.1079241
Publisher site
See Article on Publisher Site

Abstract

In this study, four important techniques for estimating the non-process energy (heating, cooling, lighting and ventilation) in industrial and manufacturing buildings were examined. The building energy data for two industrial facilities were collected and analysed. The building non-process energy includes lighting, heating, cooling and ventilation. The power intensity (kW/m2) for each energy type use was estimated using two or more methods (direct measurement of non-process energy, direct measurement of process energy, regression and simulation) and then analysed. The results of these estimates were close to each other which indicate that any method can be used based on available information. This non-process energy needs to be clearly defined to allow more quantitative improvements. The utilization of percentage of non-process energy in industrial and manufacturing plants is an important potential source of energy and cost improvements. Previous analysis of industrial energy use often expressed non-process energy as a percentage of total energy, but without clear values of actual non-process energy. This information is a low value since the actual non-process energy is then dominated by the dominator which is unspecified.

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jul 2, 2016

Keywords: industrial building energy consumption; non-process energy; estimating industrial manufacturing facility energy consumption; overhead energy

References

  • Analysis of energy use in building services of the industrial sector in California: Two case studies
  • Electricity consumption in the industrial sector of Jordan: Application of multivariate linear regression and adaptive neuro-fuzzy techniques
  • Contrasting the capabilities of building energy performance simulation programs
  • Methodology to estimate building energy consumption using EnergyPlus benchmark models
  • An applied artificial intelligence approach towards assessing building performance simulation tools
  • Applying computer-based simulation to energy auditing: A case study