Analisis Perbandingan Metode Prakiraan Kebutuhan Energi Listrik Menggunakan Moving Average dan Exponential Smoothing di PT. PLN Distribusi Jawa Timur. (Comparative Analysis of Electrical Energy Needs Forecasting Methods Using Moving Average and Exponential Smoothing at PT. East Java Distribution PLN).

Darmawan, Hima (2023) Analisis Perbandingan Metode Prakiraan Kebutuhan Energi Listrik Menggunakan Moving Average dan Exponential Smoothing di PT. PLN Distribusi Jawa Timur. (Comparative Analysis of Electrical Energy Needs Forecasting Methods Using Moving Average and Exponential Smoothing at PT. East Java Distribution PLN). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Energi yang sangat dibutuhkan dalam kegiatan sehari-hari salah satunya adalah listrik. Jumlah konsumsinya dari waktu ke waktu cenderung meningkat dan tidak dapat ditentukan secara pasti. Meningkatnya penggunaan energi listrik ini mendorong pihak penyedia untuk mampu mengelola dan mendistribusikan energi listrik secara merata. Agar tidak mengganggu aktivitas masyarakat atau konsumen yang memerlukan energi listrik yang akhirnya dapat menggaggung dan merugikan konsumen. Yang jadi permasalahannya adalah jika energi yang diproduksi dan disalurkan lebih kecil hasilnya keperluan energi yang dibutuhkan atau pemakai listrik tidak dipenuhi dengan maksimal, dan apabila daya dari pembangkit yang disalurkan terlalu berlebihan dari pada yang dibutuhkan, hasilnya akan menjadi penghamburan energi pada PLN. prakiraan kebutuhan energi listrik sangat dibutuhkan untuk memperkirakan kebutuhan energi listrik dan dapat meminimalisir pemborosan energi pada pembangkit dan gangguan kelistrikan pada konsumen. Dalam penelitian ini akan membandingkan 2 metode yaitu Simple Moving Average dan Sigle Exponential Smoothing agar dapat menemukan nilai error MAPE yang rendah/nilai peramalan yang tepat di wilayah Jawa Timur. Berdasarkan pengujian yang telah dilakukan menggunakan software Ms. Excel dengan menggunakan data tahunan menghasilkan bahwa metode Single Exponential Smothing menghasilkan nilai error yang lebih kecil yaitu pada sektor rumah tangga sebesar 0,040, industri 0,051, bisnis 0,064. Dari penelitian ini dapat dimengerti model Single Exponential Smoothing dapat menghasilkan MAPE yang lebih kecil.

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