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All Journal Jurnal technoscientia Panrita Abdi - Jurnal Pengabdian pada Masyarakat Journal Publicuho Borneo Engineering : Jurnal Teknik Sipil Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Jurnal Penelitian Pendidikan Fisika JURNAL TEKNOLOGI TECHNOSCIENTIA Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Manajemen Rekayasa (Journal of Engineering Management)
Hasmina Tari Mokui
Universitas Halu Oleo
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Journal : JURNAL TEKNOLOGI TECHNOSCIENTIA

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EXTREME LEARNING MACHINE: APLIKASI PADA SHORT TERM LOAD FORECASTING Mokui, Hasmina Tari
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 2 No 2 Februari 2010
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v2i2.444

Abstract

Accurate load forecasting becomes an important task for operating and planning of a power system to maintain the security of power supply dispatched to the consumers. This paper proposes an advanced method, namely Extreme Machine Learning (ELM), to forecast load in short time period. It is observed that implementation of the ELM can redu-ce cost and time significantly. Comparison results with a well known algorithm, called the Back Propagation (BP), show that the ELM can converge a hundred times faster than BP. In addition, the ELM needs 100 hidden neurons while the BP needs 2 hidden neurons to achieve similar result. This reveals that the number of hidden neurons is not a problem for ELM as long as there is sufficient memory to perform its computation.
Co-Authors Abdul Kadir Achmad Nur Aliansyah Anshari Nur, Muh. Nadzirin Cindy Puspitafuri DANU SALAM Eko Wahyu Prasetyo Eso, Rosliana Faldin Irmayanti Kasman Kasman La Ode Ahmad Nur Ramadhan La Ode Kasad La Ode Santiaji Bande Luther Pagiling Mansur Maudhy Satyadharma Mustamin Mustarum Musaruddin Mustarum Musaruddin Nur Rahmadani Pebi Wahyudin Rachman, Ranno Marlany Saefuddin Saefuddin Saidman, La Ode Samuel Jie St. Nawal Jaya Tachrir Tachrir Wa Ode Siti Nur Alam Zulfikar Putera