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 173      Downloads 186
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
Article Tools
Follow on us
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.
ANFIS, MAPE, Electrical Load
To cite this article
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. doi: 10.11648/j.jeee.20190701.11
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Guilin Zheng, Li Zhang. “The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM”.2015. International Journal of Online Engineering, Vol 11 pages 8.
Donate J P, Cortez P, Sánchez G G, et al., “Time series forecast- ing using a weighted cross-validation evolutionary artificial neural network ensemble”.2007. Neurocomputing, Vol. 109, No. 3, pp. 27-32.
R. Palivonaite, M. Ragulskis, “Short-term time series algebraic forecasting with internal smoothing”.2014. Neurocomputing, Vol. 127, No. 15, pp. 161-171.
J. Shao, “Application of an artificial neural network to improve short-term road ice forecasts”.1998. Expert Syst. Appl., Vol. 14, Issue 4, pp. 471-482.
Gan-qiong Li, Shi-wei Xu, Zhe-min Li. 2010. “Short-Term Price Forecasting For Agro-product Using Artificial Neural Network”. Agriculture and Agricultural Science Procedia 1 (2010) 278-287.
S. Kuusisto, M. Lehtokangas, J. Saarinen and K. Kashi. “Short Term Electric Load Forecasting Using a Neural Network with Fuzzy Hidden Neurons”. Neural Comput & Applic, 6: 42-56, 1997.
Shiliang Sun. “Traffic Flow Forecasting Based on Multitask Ensemble Learning”. GEC’09, June 12-14, 2009, Shanghai, China.
Ivan Simeonov, Hristo Kilifarev and Rajcho llarionov. “Algorithmic Realization of System for Short-term weather forecasting”. CompSysTech’07. June 14-15, 2007. University of Rousse, Bulgaria.
Chiristopher Kiekintveld, Jason Miller, Patrick R. Jordan and Michael P. Wellman. “Forecasting Market Price in a Supply Chain Game”. AAMAS’07 May 14-18 2007, Honolulu, Hawai’i, U.S.A.
Hamidie, Kafahri Arya. 2009. “Metode Koefisien Energi Untuk Peramalan Beban Jangka Pendek Pada Jaringan Jawa Madura Bali”. Jurnal Electrical Engineering of Universitas Indonesia.
Hesham K. Alfares, Mohammad Nazeeruddin. 2002. “Electric load Forecasting: literature survey and classification of methods”. International Journal of System Science, Volume 33 pages 23-34.
Minaye, Emiyamrew dan Matewose, Melaku. 2013. “Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply”. International Journal of Science and Research (IJSR), pp. 2319- 2324.
Karthika. B. S, Paresh Chandra Deka. 2015. “Prediction of Air Temperature by Hybridized Model (Wavelet-ANFIS) Using Wavelet Decomposed Data”. International Conference on Water Resources, Coastal and Ocean Engineering. Aquatic Procedia 4, pp. 1155-1161.
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.
Hsin-hung Lee, Iosif I. Shinder, John D. Wright, Michael R. Moldover. 2014. “Application of ANFIS Method to the Non-nulling Calibration of Multi-Hole Pitot Tube”. 37th National Conference on Theoretical and Applied Mechanics (37th NCTAM 2013) & the 1st International Conference on Mechanics (1st ICM). Procedia Engineering 79, pp. 125-132.
Yan-liang Zhang, Jun-Hui Lei. 2017. “Prediction of Laser Cutting Roughness in Intelligent Manufacturing Mode Based on ANFIS”. Procedia Engineering 174 (2017) 82-89.
Liu Junli. “Research on the Coal-rock Interface Recognition Based on the Fusion of Multi-information with ANFIS”. China Coal, 2014, 12: 56-59.
Log sheet by 2015, PT PLN (Persero) Area Pengatur Beban Bali.
Log sheet by 2016, PT PLN (Persero) Area Pengatur Beban Bali.
Log sheet by 2017, PT PLN (Persero) Area Pengatur Beban Bali.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186