Feature engineering and long short-term memory for energy use of appliances prediction
| Main Authors: | I Wayan Aditya Suranata, I Nyoman Kusuma Wardana, Naser Jawas, I Komang Agus Ady Aryanto |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5880002 |
Daftar Isi:
- 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 root mean squared error (RMSE) and mean average error (MAE), respectively.