Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method
Journal of Electrical and Electronic Engineering
Volume 7, Issue 1, February 2019, Pages: 1-7
Received: Oct. 31, 2018;
Accepted: Nov. 27, 2018;
Published: Jan. 24, 2019
Views 300 Downloads 196
I. Gde Made Yoga Semadhi Artha, Department of Electrical Engineering, Udayana University, Denpasar, Indonesia
Ida Bagus Gede Manuaba, Department of Electrical Engineering, Udayana University, Denpasar, Indonesia
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.
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, Journal of Electrical and Electronic Engineering.
Vol. 7, No. 1,
2019, pp. 1-7.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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