Prediksi Daya Listrik Harian Pada Output PLTS PPSDM Migas Dengan Metode Artificial Neural Network

Ongkida, Dimas (2024) Prediksi Daya Listrik Harian Pada Output PLTS PPSDM Migas Dengan Metode Artificial Neural Network. Diploma thesis, Institut Teknologi Sepuluh Nopember.

	Text 2040201147-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (5MB) | Request a copy
	Text 2040201147-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (5MB) | Request a copy

Abstract

Pusat Pengembangan Sumber Daya Manusia Minyak dan Gas Bumi (PPSDM Migas) merupakan lembaga yang berfokus pada peningkatan kompetensi tenaga kerja di sektor minyak dan gas bumi di Indonesia. Sebagai bagian dari Kementerian Energi dan Sumber Daya Mineral (ESDM), PPSDM Migas telah memulai inisiatif penggunaan energi terbarukan dengan memasang panel surya untuk memenuhi kebutuhan listrik di kantor dan fasilitas lainnya. Panel surya di PPSDM Migas Cepu memiliki kapasitas 280 Wp dengan efisiensi 19,6%, dengan menggunakan inverter model lama dimana inverter tersebut belum ada serial komunikasi sehingga pengambilan data dilakukan secara manual serta sistem panel surya yang digunakan belum dilengkapi dengan sistem pemantauan yang terintegrasi secara real-time. penelitian ini bertujuan untuk pemantauan secara realtime dan memprediksi daya listrik harian pada keluaran (output) pada Pembangkit Listrik Tenaga Surya (PLTS) di PPSDM Migas menggunakan metode Artificial Neural Network (ANN). metode ANN dipilih karena kemampuannya untuk belajar dari data historis dan menemukan pola-pola kompleks yang tidak terlihat dengan metode konvensional. Alat ini dilengkapi penggunaan website pemantauan pada sistem PLTS bisa dipantau secara real-time dan online,serta dapat memberikan data komprehensif terkait daya keluaran (output) pada PLTS sekitar laboratorium PPSDM Cepu. Setelah penerapan sistem komunikasi pada inverter, data yang dihasilkan dan dikumpulkan mencakup arus, tegangan, suhu, dan daya. Data ini sepenuhnya berasal dari hasil pengukuran inverter terhadap panel surya (PV), untuk mengetahui sistem PLTS dapat menghasilkan listrik. Dengan integrasi sistem ini, pemantauan dapat dilakukan secara online dan memberikan informasi komprehensif mengenai performa PLTS di laboratorium PPSDM Migas Cepu. Alat ini dapat mengetahui daya listrik yang dihasilkan sistem panel surya (PV) pada PLTS PPSDM Migas per harinya menggunakan machine learning Artificial Neural Network dengan akurasi 99%.
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The Oil and Gas Human Resources Development Center (PPSDM Migas) is an institution that focuses on increasing workforce competency in the oil and natural gas sector in Indonesia. As part of the Ministry of Energy and Mineral Resources (ESDM), PPSDM Migas has started an initiative to use renewable energy by installing solar panels to meet electricity needs in offices and other facilities. The solar panels at PPSDM Migas Cepu have a capacity of 280 Wp with an efficiency of 19.6%, using an old model inverter where the inverter does not have serial communication so data collection is done manually and the solar panel system used is not equipped with a real integrated monitoring system. -time. This research aims to real-time monitoring and predicting daily electrical power output at Solar Power Plants (PLTS) at PPSDM Migas using the Artificial Neural Network (ANN) method. The ANN method was chosen because of its ability to learn from historical data and discover complex patterns that are not visible with conventional methods. This tool is equipped with the use of a monitoring website on the PLTS system which can be monitored in real-time and online, and can provide comprehensive data regarding the output power of the PLTS around the PPSDM Cepu laboratory. After implementing the communication system on the inverter, the data generated and collected includes, current, voltage, temperature, and power. This data comes entirely from the results of inverter measurements of solar panels (PV), to find out whether the PLTS system can produce electricity. With the integration of this system, monitoring can be carried out online and provide comprehensive information regarding PLTS performance in the Cepu Oil and Gas PPSDM laboratory. This tool can determine the electrical power produced by the solar panel (PV) system at PLTS PPSDM Migas per day using Artificial Neural Network machine learning with 99% accuracy.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Sistem Monitoring Energi, Pembangkit Listrik Tenaga Surya, Website, PPSDM, Migas
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1056 Solar power plants. Ocean thermal power plants T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1087 Photovoltaic power generation
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Dimas Ongkida
Date Deposited:	29 Aug 2024 05:57
Last Modified:	29 Aug 2024 05:58
URI:	http://repository.its.ac.id/id/eprint/115558

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