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TECHNICAL PAPERS

Generalized Model of Prediction of Natural Gas Consumption

[+] Author and Article Information
S. Gil

Distribution Division of ENARGAS, ENARGAS–Natural Gas Regulatory Agency of Argentina, Suipacha 636-4p. (1008) Cap. Fed.-Argentina

J. Deferrari

Distribution Division of ENARGAS, ENARGAS–Natural Gas Regulatory Agency of Argentina, Suipacha 636-4p. (1008) Cap. Fed.-Argentina

J. Energy Resour. Technol 126(2), 90-98 (Jun 22, 2004) (9 pages) doi:10.1115/1.1739239 History: Received April 01, 2002; Revised November 01, 2003; Online June 22, 2004
Copyright © 2004 by ASME
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References

Khotanzad,  A., Elragal,  H., and Lu,  Tsun-Liang, 2000, “Combination of Artificial Neural-Network Forecasters for Prediction of Natural Gas Consumption,” IEEE Trans. Neural Netw., 11, (2), pp. 464–473.
Brown,  R. H., Matin,  L., Kharout,  P., and Piessens,  L. P., 1996, “Development of Artificial Neural-network Models to Predict Daily Gas Consumption,” A.G.A. Forecasting Rev., 5, pp. 1–22.
Suykens,  J., Lemmerling,  Ph., Favoreel,  W., De Moor,  B., Crepel,  M., and Briol,  P., 1996, “Modeling the Belgian Gas Consumption Using Neural Networks,” Neural Processing Lett., 4, (3), pp. 157–166, Dec.
Natural Gas Regulatory Framework (Marco Regulatorio del Gas Ley 24.076 de la Nación Argentina)-www.enargas.gov.ar
Gil, S., and Deferrari, J., 1999. “Modelo de Predicción de Consumo de gas natural en la República Argentina,” Petrotecnia (Revista del Instituto Argentino del Petróleo y del Gas) XL , 3, Sup. Tecn. 1, 1-June.
Gil, S., and Deferrari, J., 2001, “Análisis de Situaciones de Riesgo en el Abastecimiento de Gas Natural al Gran Buenos AiresLatin American and Caribbean Gas and Electricity Congress-Punta del Este-Uruguay-March. www.iapg.org.ar
Standing,  T. H., 2001, “Climate Change Projections Hinge on Global CO2, Temperature Data,” Oil & Gas J., Oil and Gas Journal.
Gil, S., and Rodrı́guez, E., 2001 Fisica re-Creativa, Prentice Hall, Buenos Aires.
Spiegel, M. R., 1958 Applied Mathematics for Engineers and Scientists, McGraw Hill, NY.
Hsu, Hwe P., 1984 Applied Fourier Analysis, Int. Thompson Pub. Co. NY.

Figures

Grahic Jump Location
Average annual consumption per user, for residential (R) and commercial (C) users, for the region of the Greater Buenos Aires (GBA) supplied by the distribution company MetroGas. The values presented here are the average daily consumption for each year. We observe an almost constant behavior, with a slight decreasing trend. The lines are fits to the data using expression (3).
Grahic Jump Location
Variation in the number of users, Commercial (C) and Residential (R), as a function of time. These data correspond to the region of GBA supplied by MetroGas. The lines are fits to the data using expression (4).
Grahic Jump Location
Variation in the number of users as a function of time. These data correspond to the region of GBA supplied by MetroGas. The continuous line is a fit to the data using expression (4).
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Variation of the daily total firm consumption (residential, commercial, etc.), represented by Qi, as a function of the mean temperature, 〈T〉, for the region of GBA supplied by MetroGas, for all the working days over the years 1996 to 2000. The circles are the measured data and the continuous curve is a fit to the data using expression (10).
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Representation of monthly residential consumption (MetroGas 1996-2000) as a function of the parameter ϕ. The line is a linear fit to the data.
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Comparison of distribution (A3) (circles) and (A4) continuous curve, both referred to the left vertical axis. In this figure we also show their respective derivatives referred to the right vertical axis.
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Comparison of monthly consumption (circles), obtained by simulation using Monte Carlo Technique, for the case of GBA, as a function of the average effective temperature. The dashed blue curve is the fit obtained using expression (10). The prediction of the monthly distribution is indicated by the continuous curve obtained using expressions (12) and (13).
Grahic Jump Location
Comparison of daily consumption, obtained by simulation using Monte Carlo Technique, for the case of GBA, as a function of the average effective temperature. The dashed blue curve is the fit obtained using expression (10). The prediction of the monthly distribution is indicated by the continuous curve obtained using expressions (12) and (13).
Grahic Jump Location
Monthly distribution of consumption (firm component) for the case of GBA supplied by MetroGas (symbols) as a function of the monthly average effective temperature, for all the working days over the years 1996 to 2000. The continuous curve is a fit obtained using expression (13).
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Distribution of the monthly mean temperatures for the GBA, the vertical error bars represent the values of the corresponding standard deviation, σmonth. The curve is a fit of the data using the function: Tmonth=a+b.cos(c.month+d).
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Distribution of daily mean temperatures for the month of July for the GBA. This histogram was obtained using all the observed temperatures for this month for the years 1944 to 2000. The continuous curve is a fit to the data using a normal distribution.
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Estimated optimum reserved capacity for the Greater Buenos Aires supplied by MetroGas predicted by the model (line), obtained by requiring that the peak consumption does not exceed this value more than one day in twenty years. The symbols (circles) represent the actual observed maximum consumption for those years.
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Probability of occurrence of firm consumption for a given year for the region of GBA supplied by MetroGas. The horizontal axis represents the total consumption in Dm3/day and the vertical axis is the probability expressed in number of days per year that a given scenario of consumption can occur in a year. The dashed curve is the prediction of the model for the year taken as reference (2000), the square symbols are the observed data for that year. The heavy continuous curve is the prediction of the model for a future year. In particular, the area between the heavy curve and the vertical line at ordinate Q0, represents the probability that the consumption in this region exceeds Q0 for the year under consideration.
Grahic Jump Location
Comparison of the observed total firm consumption of natural gas in (open circles) for the region of GBA supplied by MetroGas and the prediction of the model (curve), the units of the vertical axis are in Dm3/day=1000 m3/day. The vertical grid corresponds to the beginning of the week (Sundays). The interval of time presented in this plot (from April to July, 1997) spans the autumn and the beginning of winter.
Grahic Jump Location
Variation of the daily total firm consumption, Qi, as a function of the effective temperature, Teff, for the region of GBA supplied by MetroGas, for all the working days over the years 1996 to 2000. The symbols (circles) are the measured data and the curve is a fit to the data using expression (10).

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