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All Journal WARTA INVOTEC Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Elementer (Elektro dan Mesin Terapan) Jurnal Inovasi Bisnis (Inovbiz) Jurnal Riset Teknologi dan Inovasi Pendidikan (Jartika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Jurnal Nusantara Mengabdi Warta LPM Jurnal INFOTEL
Bandiyah Sri Aprillia
Universitas Telkom
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term Kharisma Bani Adam; Desri Kristina Silalahi; Bandiyah Sri Aprillia; Husayn Aththar Adhari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.186 KB) | DOI: 10.29207/resti.v6i4.4134

Abstract

Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output
Co-Authors Achmad Rizal Ahmad Sugiana Ayu Siti Aminah Brahmantya Aji Pramudita Desri Kristina Silalahi Husayn Aththar Adhari Irwan Purnama Iwan Iwut Tritoasmoro Iwan Kustiawan JANGKUNG RAHARJO Kevin Octavianov Abdurohman Kharisma Bani Adam Mohamad Ramdhani Muhammad Agung Foury Rigoursyah Muhammad Hablul Barri Muhammad Rafiqy Zulfahmi Porman Pangaribuan Prisma Megantoro Wahmisari Priharti Yossi Safitri