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A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices

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Abstract

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn from historical data, especially when price elasticity of demand is not significant. This paper proposes an integrated adaptive fuzzy inference system (FIS) to estimate and forecast long-term electricity consumption when prices experience large increase. To this end, first a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Logarithmic linear regressions are estimated with historical data and used to construct an initial first-order TS-FIS. Then, in the adaptation phase, expert knowledge is used to define new fuzzy rules which form a new secondary FIS for electricity forecasting. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual electricity consumption in Iran where removing energy subsidies has resulted in a hike in electricity prices. Gross domestic product (GDP), population and electricity price are three inputs for the initial TS-FIS. A questionnaire survey was conducted to collect the expert estimation on possible change in electricity per capita, change in electricity intensity and the ratio of GDP elasticity to population elasticity when price hikes. Based on the information collected, a fuzzy rule base is formed and used to construct the secondary FIS which is used for electricity forecasting until 2016. Furthermore, the performance of the proposed model of this paper is compared with three other models namely ANFIS, ANN and one-stage regression in terms of their mean absolute percentage error. The comparison shows a superior performance for the proposed FIS model.

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Notes

  1. R-squared or the coefficient of determination is the percentage of the total variation in the electricity consumption that is explained by the regression model 2.

  2. Having in hand x and x' as actual and estimated data, respectively, the mean absolute percentage error (MAPE) = 1/n∑ t=1:n [|x t -x' t |/x t ]. Scaling the output, MAPE method is the most suitable method to estimate the relative error because it accounts for the different scales that may be existed in outputs.

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Acknowledgments

The authors would like to thank the referees for their useful comments. The work on this paper has been funded by Najafabad Branch, Islamic Azad University, Esfahan, through a research project. The financial support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Esfahan, Iran

    S. M. Sajadi

  2. Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    S. M. Asadzadeh

  3. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    V. Majazi Dalfard

  4. Department of Management, Imam Khomeini International University, Qazvin, Iran

    M. Nazari Asli

  5. Young Researchers Club, Roudbar Branch, Islamic Azad University, Roudbar, Iran

    S. Nazari-Shirkouhi

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  1. S. M. Sajadi
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Correspondence to S. Nazari-Shirkouhi.

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Sajadi, S.M., Asadzadeh, S.M., Majazi Dalfard, V. et al. A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices. Neural Comput & Applic 23, 2405–2416 (2013). https://doi.org/10.1007/s00521-012-1197-6

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