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Research Papers: Energy Systems Analysis

Energy Management Techniques for Small- and Medium-Sized Companies (ESDA2006-95808)

[+] Author and Article Information
Elena Giacone

 Politecnico di Torino, Department of Energetics, Corso Duca degli Abruzzi 24, Torino 10129, Italyelena.giacone@polito.it

Salvatore Mancò

 Politecnico di Torino, Department of Energetics, Corso Duca degli Abruzzi 24, Torino 10129, Italyelena.giacone@polito.it

Pietro Gabriele

 Former Fiat Auto Energy and Ecology manager, Via Abate Villa 15, Andezeno (TO) 10020, Italypt.gabriele@alice.it

J. Energy Resour. Technol 130(1), 012002 (Feb 04, 2008) (7 pages) doi:10.1115/1.2835614 History: Received July 17, 2006; Revised September 24, 2007; Published February 04, 2008

Energy management in the industrial context is an important factor to attain energy savings as well as environmental efficiency. Often, linear regression models quite well represent the consumption of energy carriers and statistical process control (SPC) techniques, such as the cumulative sum (CUSUM) plot and Shewhart-like control charts, are currently applied to identify when a system changes the way energy is consumed. Despite the fact that SPC is widely applied in many fields, there is a lack of published material in energy management. The purpose of this paper is to widen the SPC techniques to be applied to energy management. Particular emphasis is given to small- and medium-sized enterprises since energy data are limited and generally known at system level. The CUSUM of the recursive residuals is proposed as the main tool for the analysis of energy consumption data, both for the historical and the monitoring phases. In addition, tabular CUSUM and EWMA control charts are also included.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Thermal energy consumption as a function of HDDs and building parameters

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Figure 2

CUSUM plot of the residuals

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Figure 3

Recursive residuals CUSUM chart for monitoring process mean (k=0.5, h=4)

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Figure 4

Recursive residuals CUSUM chart for monitoring process variability (k=0.5, h=4)

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Figure 5

Backward recursive residuals CUSUM chart for monitoring process mean (k=0.5, h=4)

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Figure 6

Cumulative electric power newly installed

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Figure 7

Tabular CUSUM control chart for monitoring process mean (k=0.5, h=4)

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Figure 8

EWMA control chart for monitoring process mean (λ=0.16, L=2.519)

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