Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets.
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 stud...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317514 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849315664876011520 |
|---|---|
| author | Qing Zeng Fang Peng Xiaojuan Han |
| author_facet | Qing Zeng Fang Peng Xiaojuan Han |
| author_sort | Qing Zeng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ac74bff2d514aeb860ea9dc5a53dcc2 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3ac74bff2d514aeb860ea9dc5a53dcc22025-08-20T03:52:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e031751410.1371/journal.pone.0317514Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets.Qing ZengFang PengXiaojuan HanGlobal 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.https://doi.org/10.1371/journal.pone.0317514 |
| spellingShingle | Qing Zeng Fang Peng Xiaojuan Han Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. PLoS ONE |
| title | Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. |
| title_full | Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. |
| title_fullStr | Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. |
| title_full_unstemmed | Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. |
| title_short | Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets. |
| title_sort | towards sustainable architecture enhancing green building energy consumption prediction with integrated variational autoencoders and self attentive gated recurrent units from multifaceted datasets |
| url | https://doi.org/10.1371/journal.pone.0317514 |
| work_keys_str_mv | AT qingzeng towardssustainablearchitectureenhancinggreenbuildingenergyconsumptionpredictionwithintegratedvariationalautoencodersandselfattentivegatedrecurrentunitsfrommultifaceteddatasets AT fangpeng towardssustainablearchitectureenhancinggreenbuildingenergyconsumptionpredictionwithintegratedvariationalautoencodersandselfattentivegatedrecurrentunitsfrommultifaceteddatasets AT xiaojuanhan towardssustainablearchitectureenhancinggreenbuildingenergyconsumptionpredictionwithintegratedvariationalautoencodersandselfattentivegatedrecurrentunitsfrommultifaceteddatasets |