Forecasting the Electricity Consumptions of PLN UP3 Cengkareng using Deep Learning
DOI:
https://doi.org/10.32736/sisfokom.v13i1.1849Keywords:
Deep Learning, Electricity Consumption, Long ShortTerm Memory, PLN, PredictionAbstract
The consumption of electrical energy for the community every year has increased including the electricity consumption of PLN UP3 Cengkareng customers. Therefore, PLN UP3 Cengkareng must supply electricity to customers in all categories such as Social Category, Household Category, Business Category, Industry Category and Government Category. With customer needs that continue to increase, it is necessary to forecast future electricity needs, so that PLN UP3 Cengkareng can provide the required electrical power. For this reason, it is necessary to predict the electricity demand. This research was conducted to forecast the electricity demand of UP3 Cengkareng by using the Deep Learning Model Long Short-Term Memory (LSTM). The data set used in this study was taken from the PLN UP3 Cengkareng information system, for 10 years, the period from 2012 to 2021. The data used is divided into 2 categories, namely 70% training data and 30% testing data. The results obtained from this prediction are 96,689, with an average neuron value of 32 and an epoch value of 10.References
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Buku Statistik Ketenagalistrikan PT PLN Persero Dan Kementerian Energi Dan Sumber Daya Mineral Direktorat Jenderal Ketenagalistrikan
PT.PLN (Persero), Rencana Usaha Penyediaan Tenaga Listrik (RUPTL) PT. PLN (Persero) Tahun 2021-2030, Indonesia
PT.PLN (Persero), Rencana Penyediaan Tenaga Listrik (RUPTL) PT. PLN (Persero) Tahun 2015-2028, Indonesia
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