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All Journal Syntax Literate: Jurnal Ilmiah Indonesia
Muhammad, Yasyfin Nur
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Humanities Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Hybrid KNN-LSTM Modeling for Short-Term Feeder Peak Load Forecasting Muhammad, Yasyfin Nur; Kartini, Unit Three; Peni, Hapsari
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i4.58191

Abstract

Load forecasting is important in power system planning and management. Accurate forecasting is key in maintaining the balance of energy supply and demand. This research develops a hybrid KNN-LSTM method for load forecasting using historical load and voltage data. KNN is used in finding local patterns and LSTM is used in capturing long-term patterns. The result is that the KNN-LSTM method provides MSE 30289.4952, RSME 174.0387, and MAE 98.9081. These results are better than the KNN and LSTM methods alone. In addition, by adding the voltage feature, the prediction result increases by 50.5%. Keywords: Load forecasting, KNN, LSTM, KNN-LSTM
Co-Authors Hapsari Peni Unit Three Kartini