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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Teknika: Jurnal Sains dan Teknologi Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Journal of Systems Engineering and Management
Alief Maulana
Universitas Sultan Ageng Tirtayasa
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Journal of Systems Engineering and Management

 1 
Solar panel optimization using maximum power point tracking based on Artificial Bee Colony algorithm Hartono Hartono; Yusraini Muharni; Wahid Diana Tasdik; Alief Maulana; Irma Saraswati
Journal of Systems Engineering and Management Vol 1, No 1 (2022)
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/joseam.v1i1.17530

Abstract

Solar panels are a renewable energy generator widely used by the wider community. However, solar panels have a disadvantage in operation, namely very low efficiency. One way to improve the performance of solar panels is to find the MPP (Maximum Power Point) on the V-P curve of the solar panel. This study aims to find the maximum point of the solar panel so that the output power is maximized. This research has succeeded in making a model of a solar panel-based MPPT system using the Artificial Bee Colony method using a DC-DC converter of the choke type. The model is made using Solarland 200 WP solar panels. The difference in output power based on the test results obtained the results of the power efficiency generated by solar panels using MPPT on changes in irradiation with an average fixed temperature of 99.06%. At temperature changes with constant irradiation, the average is 99.12%.
Load Forecasting Energi Listrik Provinsi Banten Tahun 2022-2030 Menggunakan Metode Backpropagation Neural Network Hartono Hartono; Yusraini Muharni; Irvan Setiawan; Irma Saraswati; Alief Maulana
Journal of Systems Engineering and Management Vol 2, No 1 (2023)
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/joseam.v2i1.19265

Abstract

Kebutuhan energi listrik dimasa yang akan datang membutuhkan suatu perencanaan dan juga prediksi yang baik. Oleh sebab itu dibutuhkan perencanaan secara sistematis dengan berdasarkan data historis. Penelitian ini menggunakan neural network dengan algoritma backpropagation untuk melakukan prakiraan kebutuhan energi listrik Provinsi Banten di masa yang akan datang. Penelitian ini bertujuan membandingkan hasil prakiraan metode jaringan syaraf tiruan (JST) dengan RUPTL PLN 2021-2030. Setelah dilakukan proses perhitungan dan simulasi diperoleh hasil prakiraan kebutuhan energi listrik Provinsi Banten pada tahun 2021–2030 dengan metode JST diperkirakan akan mengalami kenaikan sebesar 30613.41 GWH pada tahun 2030 dengan rata–rata pertumbuhan setiap tahun sebesar 3.24%. JST backpropagation memiliki nilai kesalahan yang lebih rendah 0.39% dibandingkan dengan RUPTL PT PLN sebesar 2.74%. Berdasarkan kriteria mean average percentage error (MAPE) hasil perakiraan keseluruhan JST backpropagation dan RUPTL PT PLN berada pada kategori dapat diterima dan sangat baik.
Co-Authors Aldi Muhamad Fikri Alfirdaus Zaharda Arief Izzuddin Ceri Ahendyarti Fahrizal, Rian Hamdan Kelana Aziz Imamul Muttakin Irma Saraswati Irvan Setiawan Jaga Sobar Julianto M. Miftah Baihaqi Muhammad Sadikin Muhammad Sadikin Muharni, Yusraini Rocky Alfanz Rohmadi Rohmadi romi wiryadinata Wahid Diana Tasdik Yusraini Muharni