Model energy consumption ratio.
<div><p>Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation s...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| _version_ | 1852021028638687232 |
|---|---|
| author | Qing Zeng (494174) |
| author2 | Fang Peng (327071) Xiaojuan Han (3278601) |
| author2_role | author author |
| author_facet | Qing Zeng (494174) Fang Peng (327071) Xiaojuan Han (3278601) |
| author_role | author |
| dc.creator.none.fl_str_mv | Qing Zeng (494174) Fang Peng (327071) Xiaojuan Han (3278601) |
| dc.date.none.fl_str_mv | 2025-04-25T17:35:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0317514.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Model_energy_consumption_ratio_/28872176 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Biological Sciences not elsewhere classified model &# 8217 integrated variational autoencoders dependent variational autoencoder reducing energy consumption energy consumption plays towards sustainable architecture framework incorporates self adaptive gated self energy conservation sustainable development vital role tpr ). term dependencies task learning study presents study addresses proposed approach prediction accuracy method achieves key strategy integrates time heightened interest gru ). green buildings existing techniques emission reduction efficient solution contributing significantly complex patterns carbon emissions capture long attention mechanisms attention gru anomaly detection accurate prediction |
| dc.title.none.fl_str_mv | Model energy consumption ratio. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation strategies. This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. Experiments on two public green building energy consumption datasets validate the effectiveness of our proposed approach. Our method achieves a prediction accuracy of 93.2%, significantly outperforming traditional deep learning methods and existing techniques. ROC curve analysis demonstrates our model’s robustness, achieving an Area Under the Curve (AUC) of 0.91 while maintaining a low false positive rate (FPR) and high true positive rate (TPR). This study presents an efficient solution for green building energy consumption prediction, contributing significantly to energy conservation, emission reduction, and sustainable development in the construction industry.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2f2d58be0eddbab754dd672ea79d134e |
| identifier_str_mv | 10.1371/journal.pone.0317514.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28872176 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Model energy consumption ratio.Qing Zeng (494174)Fang Peng (327071)Xiaojuan Han (3278601)Science PolicyBiological Sciences not elsewhere classifiedmodel &# 8217integrated variational autoencodersdependent variational autoencoderreducing energy consumptionenergy consumption playstowards sustainable architectureframework incorporates selfadaptive gated selfenergy conservationsustainable developmentvital roletpr ).term dependenciestask learningstudy presentsstudy addressesproposed approachprediction accuracymethod achieveskey strategyintegrates timeheightened interestgru ).green buildingsexisting techniquesemission reductionefficient solutioncontributing significantlycomplex patternscarbon emissionscapture longattention mechanismsattention gruanomaly detectionaccurate prediction<div><p>Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation strategies. This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. Experiments on two public green building energy consumption datasets validate the effectiveness of our proposed approach. Our method achieves a prediction accuracy of 93.2%, significantly outperforming traditional deep learning methods and existing techniques. ROC curve analysis demonstrates our model’s robustness, achieving an Area Under the Curve (AUC) of 0.91 while maintaining a low false positive rate (FPR) and high true positive rate (TPR). This study presents an efficient solution for green building energy consumption prediction, contributing significantly to energy conservation, emission reduction, and sustainable development in the construction industry.</p></div>2025-04-25T17:35:28ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0317514.g003https://figshare.com/articles/figure/Model_energy_consumption_ratio_/28872176CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288721762025-04-25T17:35:28Z |
| spellingShingle | Model energy consumption ratio. Qing Zeng (494174) Science Policy Biological Sciences not elsewhere classified model &# 8217 integrated variational autoencoders dependent variational autoencoder reducing energy consumption energy consumption plays towards sustainable architecture framework incorporates self adaptive gated self energy conservation sustainable development vital role tpr ). term dependencies task learning study presents study addresses proposed approach prediction accuracy method achieves key strategy integrates time heightened interest gru ). green buildings existing techniques emission reduction efficient solution contributing significantly complex patterns carbon emissions capture long attention mechanisms attention gru anomaly detection accurate prediction |
| status_str | publishedVersion |
| title | Model energy consumption ratio. |
| title_full | Model energy consumption ratio. |
| title_fullStr | Model energy consumption ratio. |
| title_full_unstemmed | Model energy consumption ratio. |
| title_short | Model energy consumption ratio. |
| title_sort | Model energy consumption ratio. |
| topic | Science Policy Biological Sciences not elsewhere classified model &# 8217 integrated variational autoencoders dependent variational autoencoder reducing energy consumption energy consumption plays towards sustainable architecture framework incorporates self adaptive gated self energy conservation sustainable development vital role tpr ). term dependencies task learning study presents study addresses proposed approach prediction accuracy method achieves key strategy integrates time heightened interest gru ). green buildings existing techniques emission reduction efficient solution contributing significantly complex patterns carbon emissions capture long attention mechanisms attention gru anomaly detection accurate prediction |