Path: Top -> Journal -> Telkomnika -> 2021 -> Vol 19, No 3, June
Feature engineering and long short-term memory for energy use of appliances prediction
Oleh : I Wayan Aditya Suranata, I Nyoman Kusuma Wardana, Naser Jawas, I Komang Agus Ady Aryanto, Telkomnika
Dibuat : 2021-09-06, dengan 0 file
Keyword : appliances, feature engineering, long short-term memory, principal component analysis, prediction
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/17882
Sumber pengambilan dokumen : Web
Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively.
Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively.
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