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Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

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  • Neto, João C. do L.
  • da Costa Junior, Carlos T.
  • Bitar, Sandro D.B.
  • Junior, Walter B.

Abstract

Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the [alpha]-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs.

Suggested Citation

  • Neto, João C. do L. & da Costa Junior, Carlos T. & Bitar, Sandro D.B. & Junior, Walter B., 2011. "Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models," Energy Policy, Elsevier, vol. 39(9), pages 4947-4955, September.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:9:p:4947-4955
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    References listed on IDEAS

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    1. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    2. Bacellar, Atlas Augusto & Rocha, Brígida R.P., 2010. "Wood-fuel biomass from the Madeira River: A sustainable option for electricity production in the Amazon region," Energy Policy, Elsevier, vol. 38(9), pages 5004-5012, September.
    3. Frota, Willamy M. & Sá, José Alberto S. & Moraes, Sinfrônio S.B. & Rocha, Brígida R.P. & Ismail, Kamal A.R., 2010. "Natural gas: The option for a sustainable development and energy in the state of Amazonas," Energy Policy, Elsevier, vol. 38(7), pages 3830-3836, July.
    4. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    5. Uri, Noel D. & Flanagan, Stephen P., 1979. "Short-term forecasting of crude petroleum and natural gas production," Applied Energy, Elsevier, vol. 5(4), pages 297-310, October.
    6. Frota, Willamy M. & Rocha, Brígida R.P., 2010. "Benefits of natural gas introduction in the energy matrix of isolated electrical system in the city of Manaus - state of Amazonas - Brazil," Energy Policy, Elsevier, vol. 38(4), pages 1811-1818, April.
    7. Gonzales Chavez, S & Xiberta Bernat, J & Llaneza Coalla, H, 1999. "Forecasting of energy production and consumption in Asturias (northern Spain)," Energy, Elsevier, vol. 24(3), pages 183-198.
    8. Bitar, Sandro D.B. & da Costa Junior, Carlos T. & Barreiros, José A.L. & Neto, João C. do L., 2009. "Expansion of isolated electrical systems in the Amazon: An approach using fuzzy multi-objective mathematical programming," Energy Policy, Elsevier, vol. 37(10), pages 3899-3905, October.
    9. Uri, Noel D., 1978. "Forecasting peak system load using a combined time series and econometric model," Applied Energy, Elsevier, vol. 4(3), pages 219-227, July.
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    Cited by:

    1. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).

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