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All Journal Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Multidisiplin Indonesia
Widya Nita Suliyanti
Institut Teknologi PLN
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Veterinary

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Machine Learning-Based Daily Feeder Load Forecasting for Operation and Maintenance Strategy in 20 kV Distribution Systems Fika Ardilla Rista; Herry Nugraha; Widya Nita Suliyanti
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 5 No. 3 (2026): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v5i3.2091

Abstract

Accurate daily feeder load forecasting is essential for supporting operation and maintenance strategies in electrical distribution systems, where nonlinear and dynamic load variations may limit the performance of conventional forecasting methods. This study develops a machine learning-based framework for daily load forecasting of six 20 kV distribution feeders (PLR16–PLR21) supplied by Transformer 1 at Palur Substation, Indonesia. We used daily operational load data from January 2020 to April 2026, incorporating customer-growth information as an additional forecasting feature. We used Random Forest as the primary model and Holt–Winters Exponential Smoothing as a benchmark. We evaluated forecasting performance using MAE, RMSE, MAPE, and sMAPE. Random Forest outperformed Holt–Winters, achieving MAE of 292.45 A, RMSE of 417.85 A, and MAPE of 12.98%, compared with 720.30 A, 853.37 A, and 26.79%, respectively. The selected model was further applied to forecast feeder loading through 2028 and identify priority feeders for operation and maintenance planning. The proposed framework provides a practical, data-driven approach to feeder load forecasting and operational decision support in 20 kV distribution systems.
Analisis Statistik dan Strategi Mitigasi Gangguan Transmisi Akibat Sambaran Petir Berbasis Data Historis UPT Bandung Tahun 2021–2025 Ega Satriyo Utomo; Widya Nita Suliyanti
Jurnal Multidisiplin Indonesia Vol. 5 No. 9 (2026): Jurnal Multidisiplin Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jmi.v5i9.2914

Abstract

Sambaran petir merupakan salah satu penyebab gangguan pada saluran transmisi yang memengaruhi keandalan sistem tenaga listrik. Penelitian ini menganalisis hubungan sambaran petir dan gangguan transmisi 150 kV PT PLN (Persero) UPT Bandung periode 2021–2025 serta mengevaluasi pengaruh karakteristik menara menggunakan Electro-Geometric Model (EGM). Analisis dilakukan berdasarkan data historis dan simulasi pada tiga tower representatif, yaitu T-047, T-032, dan T-018, dengan parameter Back Flashover Rate (BFR), Back Flashover Outage Rate (BFOR), Shielding Failure Rate (SFR), dan Shielding Failure Flashover Outage Rate (SFFOR). Hasil analisis menunjukkan rata-rata sambaran petir sebesar 158.942,8 kejadian/tahun dan gangguan sebesar 19,2 kejadian/tahun, dengan hubungan keduanya berada pada kategori sedang. Hasil simulasi menunjukkan BFR berturut-turut sebesar 141,4; 163,3; dan 121,6 gangguan/100 km/tahun untuk T-047, T-032, dan T-018, sedangkan BFOR masing-masing sebesar 74,90; 17,16; dan 4,64 gangguan/100 km/tahun. Nilai SFR sebesar 0,331; 0,942; dan 0,393, sedangkan SFFOR sebesar 0,324; 0,933; dan 0,387 gangguan/100 km/tahun. Total gangguan tertinggi terdapat pada T-047 sebesar 75,22 gangguan/100 km/tahun, sehingga menjadi prioritas mitigasi. Strategi yang direkomendasikan meliputi perbaikan sistem pentanahan dengan target tahanan <5 ?, evaluasi konfigurasi shield wire pada T-032, optimalisasi lightning arrester, serta inspeksi dan pemeliharaan berbasis kondisi, dengan prioritas inspeksi sebelum periode musim hujan. Hasil penelitian menunjukkan bahwa peningkatan kualitas pentanahan menjadi prioritas utama karena nilai BFOR secara konsisten lebih tinggi dibandingkan SFFOR.
Co-Authors Ega Satriyo Utomo Fika Ardilla Rista Herry Nugraha