Journal of Electrical and Electronic Engineering

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Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method

Received: 31 October 2018    Accepted: 27 November 2018    Published: 24 January 2019
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Abstract

The development of the tourist destinations of Bali Island, especially Nusa Dua area, should be included with the development of electricity supply in the region. To facilitate a process of electrical energy planning in the region, it is necessary to forecast the electrical load in the area. Forecasting is a process to estimate future events / things to come. In this research, the forecasting of the long-term electrical load for five years and this forecasting method using ANFIS method and use ANN method as a comparison. From the simulation conducted by MAPE which resulted from forecasting the weekly electrical load using ANFIS method is 0.028% while MAPE forecasting using ANN method is 51.57%. From the comparison result, it can be said that the annual electricity load forecasting using the ANFIS method has a better forecasting accuracy than using the ANN method. The forecasting results of this transformer load is used as a reference in planning the electricity system on the Bali Island.

DOI 10.11648/j.jeee.20190701.11
Published in Journal of Electrical and Electronic Engineering (Volume 7, Issue 1, February 2019)
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

ANFIS, MAPE, Electrical Load

References
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[14] Youssef Kassem, Huseyin Camur, Engin Esenel. 2017. “Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K. Procedia Computer Science 120 (2017) 521-528.
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Author Information
  • Department of Electrical Engineering, Udayana University, Denpasar, Indonesia

  • Department of Electrical Engineering, Udayana University, Denpasar, Indonesia

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  • APA Style

    I. Gde Made Yoga Semadhi Artha, Ida Bagus Gede Manuaba. (2019). Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method. Journal of Electrical and Electronic Engineering, 7(1), 1-7. https://doi.org/10.11648/j.jeee.20190701.11

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    ACS Style

    I. Gde Made Yoga Semadhi Artha; Ida Bagus Gede Manuaba. Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method. J. Electr. Electron. Eng. 2019, 7(1), 1-7. doi: 10.11648/j.jeee.20190701.11

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    AMA Style

    I. Gde Made Yoga Semadhi Artha, Ida Bagus Gede Manuaba. Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method. J Electr Electron Eng. 2019;7(1):1-7. doi: 10.11648/j.jeee.20190701.11

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  • @article{10.11648/j.jeee.20190701.11,
      author = {I. Gde Made Yoga Semadhi Artha and Ida Bagus Gede Manuaba},
      title = {Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {7},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.jeee.20190701.11},
      url = {https://doi.org/10.11648/j.jeee.20190701.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.jeee.20190701.11},
      abstract = {The development of the tourist destinations of Bali Island, especially Nusa Dua area, should be included with the development of electricity supply in the region. To facilitate a process of electrical energy planning in the region, it is necessary to forecast the electrical load in the area. Forecasting is a process to estimate future events / things to come. In this research, the forecasting of the long-term electrical load for five years and this forecasting method using ANFIS method and use ANN method as a comparison. From the simulation conducted by MAPE which resulted from forecasting the weekly electrical load using ANFIS method is 0.028% while MAPE forecasting using ANN method is 51.57%. From the comparison result, it can be said that the annual electricity load forecasting using the ANFIS method has a better forecasting accuracy than using the ANN method. The forecasting results of this transformer load is used as a reference in planning the electricity system on the Bali Island.},
     year = {2019}
    }
    

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    T1  - Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method
    AU  - I. Gde Made Yoga Semadhi Artha
    AU  - Ida Bagus Gede Manuaba
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    DO  - 10.11648/j.jeee.20190701.11
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.jeee.20190701.11
    AB  - The development of the tourist destinations of Bali Island, especially Nusa Dua area, should be included with the development of electricity supply in the region. To facilitate a process of electrical energy planning in the region, it is necessary to forecast the electrical load in the area. Forecasting is a process to estimate future events / things to come. In this research, the forecasting of the long-term electrical load for five years and this forecasting method using ANFIS method and use ANN method as a comparison. From the simulation conducted by MAPE which resulted from forecasting the weekly electrical load using ANFIS method is 0.028% while MAPE forecasting using ANN method is 51.57%. From the comparison result, it can be said that the annual electricity load forecasting using the ANFIS method has a better forecasting accuracy than using the ANN method. The forecasting results of this transformer load is used as a reference in planning the electricity system on the Bali Island.
    VL  - 7
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