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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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Listed:
  • 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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    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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