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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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020030 |
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| author | Mona Ahamd Alghamdi Abdullah S. AL-Malaise AL-Ghamdi Mahmoud Ragab |
| author_facet | Mona Ahamd Alghamdi Abdullah S. AL-Malaise AL-Ghamdi Mahmoud Ragab |
| author_sort | Mona Ahamd Alghamdi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2929296c75f549aea672280f9d3775db |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-2929296c75f549aea672280f9d3775db2025-02-03T09:08:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017224727010.26599/BDMA.2023.9020030Predicting Energy Consumption Using Stacked LSTM Snapshot EnsembleMona Ahamd Alghamdi0Abdullah S. AL-Malaise AL-Ghamdi1Mahmoud Ragab2Information Systems Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah 21589, Kingdom of Saudi ArabiaInformation Systems Department, FCIT, KAU, Jeddah 21589, Kingdom of Saudi Arabia, and also with School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Kingdom of Saudi ArabiaInformation Technology Department, FCIT, KAU, Jeddah 21589, Kingdom of Saudi Arabia, and also with Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, EgyptThe 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020030artificial intelligence (ai)deep learning (dl)energy consumptionsnapshot ensembleprediction |
| spellingShingle | Mona Ahamd Alghamdi Abdullah S. AL-Malaise AL-Ghamdi Mahmoud Ragab Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble Big Data Mining and Analytics artificial intelligence (ai) deep learning (dl) energy consumption snapshot ensemble prediction |
| title | Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble |
| title_full | Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble |
| title_fullStr | Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble |
| title_full_unstemmed | Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble |
| title_short | Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble |
| title_sort | predicting energy consumption using stacked lstm snapshot ensemble |
| topic | artificial intelligence (ai) deep learning (dl) energy consumption snapshot ensemble prediction |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020030 |
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