Open Access Open Access  Restricted Access Subscription or Fee Access

Electricity Demand Forecasting Using Adaptive Neuro-Fuzzy Inference System and Particle Swarm Optimization


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireaco.v9i6.10810

Abstract


As a state-owned company which supplies electricity to the public, PT. PLN should continuously provide electricity in sufficient amountwith good quality and reliability. As a first step to achieve this goal, PLN should be able to estimate or predict the demand for electricity in the future. This paper proposes the combination of adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO) approaches to forecast the electricity demand. PSO is used to optimize the membership function of ANFIS by changing the parameter premise and consequent parameters on ANFIS. This proposed method is implemented in each province in Indonesia. The results indicate that the accuracy of the proposed method increased significantly compared to the standard adaptive neuro-fuzzy inferences system. It can be seen from the Mean Absolute Percentage Error (MAPE) in each method that the ANFIS is 6.8000% and the ANFIS-PSO is 5.5286%.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Electricity Demand; Forecasting; Adaptive Neuro-Fuzzy Inferences System; Particle Swarm Optimization

Full Text:

PDF


References


M. Mordjaoui, B. Boudjema, Forecasting and Modeling Electricity Demand Using ANFIS Predictor, Journal of Mathematics and Statistics, Vol. 7, n. 4, pp. 275-281, 2011.
http://dx.doi.org/10.3844/jmssp.2011.275.281

M. Syafruddin, L. Hakim, D. Despa, Metode Regresi Linier untuk Prediksi Kebutuhan Energi Listrik Jangka Panjang (Studi Kasus: Provinsi Lampung), Jurnal Informatika dan Teknik Elektro Terapan, Vol. 1, n. 2, 2014.
http://dx.doi.org/10.22146/jnteti.v5i2.233

O. Somantri, V. Suhartono, C. Supriyanto, M. Khambali, Prediksi Kebutuhan Permintaan Energi Listrik Menggunakan Neural Network Berbasiskan Algoritma Genetika, Proceedings of the 7th National Conference on Information Technology and Electrical Engineering (Page: 327 Year of Publication: 2015 ISSN: 2085-6350).
http://dx.doi.org/10.22146/jnteti.v5i2.233

Y. Gong, Y. Zhang, S. Lan, H, Wang, A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida, Water Resource Management, Vol. 30, n.1, pp. 375-391, 2016.
http://dx.doi.org/10.1007/s11269-015-1167-8

S. Kaboodvanpour, J. Amanollahi, S. Qhavami, B. Mohammadi, Assessing the Accuracy of Multiple Regression, ANFIS, and ANN Models in Predicting Dust Storm Occurences in Sanandaj, Iran, Natural Hazards, Vol. 78, n. 2, pp. 897-893, 2015.
http://dx.doi.org/10.1007/s11069-015-1748-0

A. Kayabasi, A. Akdagli, A Comparative Study on ANN, ANFIS, and SVM Methods for Computing Resonant Frequency of A-Shaped Compact Microstrip Antennas, World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, Vol. 9, n. 8 pp. 594-600, 2015.
http://dx.doi.org/10.1007/s11277-015-2321-6

R. Noori, Z. Deng, A. Kiaghadi, F. T. Kachoosangi, How Reliable Are ANN, ANFIS, and SVM Techniques for Predicting Longitudinal Dispersion Coefficient in Natura Rivers?, Journal of Hydraulic Engineering, Vol. 142, n. 1, pp. 04015039, 2015.
http://dx.doi.org/10.1061/(asce)hy.1943-7900.0001062

M. Sarvi, M. Safari, Fuzzy, ANFIS, and ICA Trained Neural Network Modeling of Ini-Cd Batteries Using Experimental Data, World Applied Programming Journal, Vol. 3, n. 3, pp. 93-100, 2013.
http://dx.doi.org/10.11591/ij-ai.v2i2.1784

M. H. Jannaty, A. Eghbalzadeh, S. A. Hosseini, Hybrid ANFIS Model for Predicting Scour Depth using Particle Swarm Optimization, Indian Journal of Science and Technology, Vol. 8, n. 22, pp. 1-7, 2015.
http://dx.doi.org/10.17485/ijst/2015/v8i22/79321

S. Mahapatra, R. Daniel, D. N. Dey, S. K. Nayak, Induction Motor Control Using PSO-ANFIS, Procedia Computer Science, Vol. 48, pp. 753-768, 2015.
http://dx.doi.org/10.1016/j.procs.2015.04.212

H. Posinho, V. Mendes, J. Catalao,Short-term Electricity Prices Forecasting in a Competitive Market by a Hybrid PSO-ANFIS Approach, Internasional Journal of Electrical Power and Energi Systems, Vol. 39, n. 1, pp. 29-35, 2012.
http://dx.doi.org/10.1016/j.ijepes.2012.01.001

D. T. Larose, Discovering Knowledge in Data: an Introduction to Data Mining (John Wiley & Sons, 2005)
http://dx.doi.org/10.1002/0471687545

J. E. Biegel, Statistics in Forecasting, Management Internasional, pp. 161-181, 1961.
http://dx.doi.org/10.1007/978-3-319-18732-7_9

J. Heizer, B. Render, Operations Management (Prentice-Hall, 1999).
http://dx.doi.org/10.2307/2584398

R. J. Hyndman, H. L. Shang, Forecasting Functional Time Series, Journal of the Korean Statistical Society, Vol. 38, n. 3, pp. 199-211, 2009.
http://dx.doi.org/10.1016/j.jkss.2009.06.002

T. Sutojo, E. Mulyanto, V. Suhatono, Kecerdasan Buatan (ANDI, 2011).
http://dx.doi.org/10.22441/sinergi.2016.3.004

W. Budiharto, D. Suhartono, Artificial Intelligence (ANDI, 2014).
http://dx.doi.org/10.1109/intellisys.2015.7361159

Li, Y., Gu, X., Application of Online SVR in Very Short-Term Load Forecasting, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 277-283.

Ramadhani, A., Dharma, A., Robandi, I., Optimization FOU of Interval Type-2 Fuzzy Inference System Using Big Bang – Big Crunch Algorithm for Short Term Load Forecasting on National Holiday Case Study: South and Central Kalimantan-Indonesia, (2015) International Review of Electrical Engineering (IREE), 10 (1), pp. 123-130.
http://dx.doi.org/10.15866/iree.v10i1.4871

Zhang, Y., Wang, J., Zhang, Y., A Hybrid Method for Short-Term Electricity Price Forecasting Based on BPNN and GSM-SVM, (2013) International Review of Electrical Engineering (IREE), 8 (5), pp. 1509-1519.

Farahat, M., Elgawed, A., Mustafa, H., Ibrahim, A., Short Term Load Forecasting Using BP Neural Network Optimized by Particle Swarm Optimization, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 450-454.

Madraky, A., Othman, Z., Hamdan, A., Hair-Oriented Data Model for Spatio-Temporal Data Mining, (2015) International Review on Computers and Software (IRECOS), 10 (1), pp. 90-101.
http://dx.doi.org/10.15866/irecos.v10i1.5198

Madraky, A., Othman, Z., Hamdan, A., Analytic Methods for Spatio-Temporal Data in a Nature-Inspired Data Model, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 547-556.

Aljabr, M., Using Data Mining Techniques in Building Dataset for Network Intrusion Detection, (2015) International Review on Computers and Software (IRECOS), 10 (7), pp. 652-659.
http://dx.doi.org/10.15866/irecos.v10i7.6121

Kumar, A., A Collaborative Web Recommendation System Based on Fuzzy Association Rule Mining Techniques, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (6), pp. 229-233.
http://dx.doi.org/10.15866/irecap.v4i6.4364

Meo, S., Zohoori, A., Vahedi, A., Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach, (2016) Energy Conversion and Management, 110, pp. 230-239.
http://dx.doi.org/10.1016/j.enconman.2015.11.062

Shankar, T., Shanmugavel, S., Karthikeyan, A., Hybrid Approach for Energy Optimization in Wireless Sensor Networks Using PSO, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (4), pp. 221-226.

Xiaowei, W., Tao, Z., Shu, T., A Novel Fault Section Location Method Based on Energy Spectrum Entropy of EMD and Fuzzy C-Means Algorithm for Small Current to Ground System, (2013) International Review of Electrical Engineering (IREE), 8 (6), pp. 1823-1832.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize