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标题：Feature engineering and long short-term memory for energy use of appliances predictio
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作者：I Wayan Aditya Suranata ; I Nyoman Kusuma Wardana ; Naser Jawas 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2021
卷号：19
期号：3
DOI：10.12928/telkomnika.v19i3.17882
语种：English
出版社：Universitas Ahmad Dahlan
摘要：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.
关键词：appliances;feature engineering;long short-term memory;principal component analysis;prediction