Main Article Content


The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity.  In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range -1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight.  According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine (ELM) method with weight optimization using Genetics Algorithm (GA) can be used in electrical load forecasting problem and give better prediction result

Article Details

How to Cite
Meilia, V., Setiawan, B. D., & Santoso, N. (2018). Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting. Journal of Information Technology and Computer Science, 3(1), 77–87.


  1. H. Muin, Writer, Mencari Solusi Strategis Untuk Kebutuhan Energi Listrik. [Performance]. 2014.
  2. G.-B. Huang, Qin-Yu and C.-K. Siew, "Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks.," in Proceedings of International Joint Conference on Neural Networks, Singapura: Nanyang Avenue, 2004.
  3. A. Hasim, "Prakiraan Beban Listrik Kota Pontianak Dengan Jaringan Syaraf Tiruan," Sekolah Pasca Sarjana Institut Pertanian Bogor, Bogor, 2008.
  4. A. S. Alencar, A. R. R. Neto and J. P. P. Gomes, "A New Pruning Method for Extreme Learning Machine via Genetic Algorithms," Applied Soft Computing, pp. 1568-4946, 2016.
  5. X. Wang, C. Wang and Q. Li, "Short-term Wind Power Prediction Using GA-ELM," The Open Electrical & Electronic Engineering Journal 2017, vol. 11, pp. 48-56, 2016.
  6. A. N. Azizah, "Penentuan Kualitas Air Sungai Menggunakan Metode Extreme Learning Machine," Fakultas Ilmu Komputer Universitas Brawijaya, Malang, 2016.
  7. J. Kusuma, M. WF and Indriati, "Optimasi Komposisi Pakan Sapi Potong Menggunakan Algoritme Genetika," DORO: Repository Jurnal Mahasiswa PTIIK Universitas Brawijaya, vol. 5, p. 15, 2015.
  8. J. Smith and E. AE, "Introduction to Evolutionary Computing," Springer, London, 2003.
  9. W. F. Mahmudy, "Dasar - Dasar Algoritme Evolusi," Program Teknologi Informasi dan Ilmu Komputer (PTIIK) Universitas Brawijaya, Malang, 2015.
  10. C. Gondro and K. B, "Application of Evolutionary Algorithms to Solve Complex Problems in Quantitative Genetics and Bioinformatics," Armidale: University of New England, New England, 2008.
  11. A. Desiani and A. M, Konsep Kecerdasan Buatan, Yogyakarta: Andi, 2006.
  12. Z.-L. Sun, T.-M. Choi, K.-F. Au and Y. Yu, "Sales Forecasting Using Extreme Learning With Applications in Fashion Retailing," Decision Support Systems, vol. 46, pp. 411-419, 2008.