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All Journal Jurnal Teknik Elektro Jurnal Teknoin JIK Jurnal Ilmu Komputer Jurnal GEMATIKA Jurnal Pengembangan Pendidikan e-Jurnal Arus Elektro Indonesia Semesta Teknika Techne : Jurnal Ilmiah Elektroteknika INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) JASEE Journal of Application and Science on Electrical Engineering SinarFe7 Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Rekayasa elektrika
Moch Gozali
Universitas Jember
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Desain Perancangan kwh meter Digital Berbasis Sistem SCADA pada Pelanggan Tegangan Menengah 20 KV di PT PLN (Persero) APJ Jember H.R.B. Moch. Gozali
Jurnal Rekayasa Elektrika Vol 9, No 3 (2011)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1155.927 KB) | DOI: 10.17529/jre.v9i3.160

Abstract

SCADA is a very powerful new technology to be applied as a tele-metering. Planning of Digital KWH Meter based on SCADA at 20 kV medium voltage customer isselected in the PLN APJ Jember, where KWH Meters arewidely used today is the Landis Gyr ZMC405C type. But it is less reliable devices along with the development of technology today. KWH Meter ZMD405CT offers more features for modern, effective and efficient to be used by PLN in the future. Design of SCADA in ZMD405CT not use a lot of cable, so they are efficient in terms of cost. Meanwhile, software for data acquisition must be balanced with the KWH meter to be used, for electrical energy measurement accuracy on the customer side can be assured.
Peramalan Beban Jangka Panjang pada Gardu Induk Bangil dengan Metode Generalized Regression Neural Network Fathurrozi, Anggit; Kalandro, Guido Dias; Rizal Chaidir, Ali; Prasetyono, Suprihadi; Gozali, Moch
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 2 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i2.461

Abstract

Given the rising energy demands, the existing electrical infrastructure, notably distribution transformers 3 and 4 at the Bangil Substation, faces the risk of overload. Accurate load forecasting is imperative to inform timely interventions like transformer replacement. This study aims to forecast the load for Transformers 3 and 4 at the Bangil Substation using 2 difference methods, comparing Feed Forward Backpropagation Neural Network (FFBNN) and Generalized Regression Neural Network (GRNN). This research also evaluates potential transformer overloads based on forecasted peak loads. This research employed a STL Decomposition to decompose monthly peak load data in each transformer into trend, seasonal and residual components and developing forecasting model for each transformer trend component data. Simultaneously, separate forecasting models were developed for the Gross Regional Domestic Product (GRDP) and the Industrial Sector of GRDP. The forecasted trend components from the transformer data were combined with the GRDP and Industrial Sector of GRDP forecasts using an approximation model. This approach aimed to approximate the monthly peak load more accurately, incorporating both energy demand trends and economic indicators. The forecasting models' accuracy was gauged using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The analysis indicates that Transformer 3 is projected to reach overload by August 2038, with a forecasted peak load of 1407.7465 A. Conversely, Transformer 4 is expected to experience overload by February 2028, with a peak load of 1269.2173 A. FFBNN exhibited superior accuracy for Transformer 3, recording a MAPE of 10.522% and MAE of 74.204. In contrast, GRNN displayed better performance for Transformer 4, achieving a MAPE of 6.051% and MAE of 46.557. Timely interventions, such as transformer replacement, are essential to mitigate potential overloads. The research underscores the importance of employing tailored forecasting approaches, emphasizing the peak load transformer data with economic indicators for more precise load approximations
Proyeksi Kebutuhan Energi Listrik dengan Metode Regresi Linear Berganda di UP3 Mojokerto Tahun 2022 sampai 2027 Mirelle Ferent Hasnitha; RB. Moch. Gozali; Suprihadi Prasetyono
JASEE Journal of Application and Science on Electrical Engineering Vol. 4 No. 02 (2023): JASEE-September
Publisher : Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v4i02.427

Abstract

Demand for the availability of electrical energy in the future requires good planning and forecasting. Based on data from BPS (Badan Pusat Statik) East Java, the population and the number of electricity customers in East Java province has increased every year. So a forecasting can be done to find out the estimated demand for electrical energy needs in the future. In this study using multiple regression analysis method to project electricity demand in UP3 Mojokerto from 2022 to 2027. The results of the 2022 projection will be compared with the actual data in 2022 for data validation with the calculation of MAPE (Mean Absolute Percent Error). The resulting MAPE value is less than 10%, which means that this multiple linear regression method is a very good method used to project electrical energy demand at UP3 Mojokerto in 2022-2027. That way, the results of projected electrical energy demand with multiple linear regression methods in UP3 Mojokerto in 2022 amounted to 4,937,341 MWh, and in 2023 to 2027 the projected electrical energy demand increased successively by 5,323,722 MWh, 5,721,631 MWh, 6,131,543 MWh, 6,553,951 MWh, and 6,989,371 MWh. With an average growth from 2022 to 2027 of 6.30%.
Co-Authors Adi Mulyadi Afirst Rosa Hariyantin Akladera Pawigo Ali Rizal Chaidir Andi Wahyudi Anugrah Prasetyo Sutrisno Arif Baihaqiy Bambang Sri Kaloko Bambang Sujanarko Dedy Kurnia Setiawan Dedy Wahyu Herdiyanto Dimas Oktasya Eka Kumala Putra Fathurrozi, Anggit Firman Jati Pamungkas Guido Dias Kalandro Gustio Riki Kartiko Anggara hafis alif bhardani Hapzi Ali Jayawarsa, A.A. Ketut Mirelle Ferent Hasnitha Muhammad Andre Budi Leksono Muhammad Farhan Muhammad Zainal Roisul Amin Riska Fita Lestari Rizal Chaidir, Ali Samsul Bachri Samsul Bachri Masmachofari Suprihadi Prasetyono Triwahju Hardianto Wahyu Muldayani Widjonarko