Feature engineering and long short-term memory for energy use of appliances prediction

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.