Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble
The ability to make accurate energy predictions while considering all related energy factors allows production plants, regulatory bodies, and governments to meet energy demand and assess the effects of energy-saving initiatives. When energy consumption falls within normal parameters, it will be poss...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020030 |
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| Summary: | The ability to make accurate energy predictions while considering all related energy factors allows production plants, regulatory bodies, and governments to meet energy demand and assess the effects of energy-saving initiatives. When energy consumption falls within normal parameters, it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy consumption. The objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily interpretable. The proposed model is an implementation of the stacked Long Short-Term Memory (LSTM) snapshot ensemble combined with the Fast Fourier Transform (FFT) and meta-learner. Hebrail and Berard’s Individual Household Electric-Power Consumption (IHEPC) dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy consumption. The model is trained, and the results measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) metrics are 0.020, 0.013, 0.017, and 0.999, respectively. The stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized errors. The results of this study show that prediction accuracy is high, and the model’s stability is high as well. The model shows that high levels of accuracy prove accurate predictive ability, and together with high levels of stability, the model has good interpretability, which is not typically accounted for in models. However, this study shows that it can be inferred. |
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| ISSN: | 2096-0654 |