Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. The...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/20/9373 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205280595345408 |
|---|---|
| author | Sijia Fu Rui Zhu Feiyang Yu |
| author_facet | Sijia Fu Rui Zhu Feiyang Yu |
| author_sort | Sijia Fu |
| collection | DOAJ |
| description | Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. Therefore, accurate and effective building energy consumption prediction is the basis of energy conservation. Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. This study proposes a Time Convolution Network model based on an attention mechanism, which combines the ability of the Time Convolution Network model to capture ultra-long time series information with the ability of the BiGRU model to integrate contextual information, improve model parallelism, and reduce the risk of overfitting. In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. The study shows that this optimized model has better accuracy than models such as LSTM, SVM, and CNN. |
| format | Article |
| id | doaj-art-44127ca2f37841259e95bdb42a339334 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-44127ca2f37841259e95bdb42a3393342025-08-20T02:11:08ZengMDPI AGApplied Sciences2076-34172024-10-011420937310.3390/app14209373Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-AttentionSijia Fu0Rui Zhu1Feiyang Yu2School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, ChinaSchool of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, ChinaSchool of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, ChinaBuilding energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. Therefore, accurate and effective building energy consumption prediction is the basis of energy conservation. Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. This study proposes a Time Convolution Network model based on an attention mechanism, which combines the ability of the Time Convolution Network model to capture ultra-long time series information with the ability of the BiGRU model to integrate contextual information, improve model parallelism, and reduce the risk of overfitting. In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. The study shows that this optimized model has better accuracy than models such as LSTM, SVM, and CNN.https://www.mdpi.com/2076-3417/14/20/9373energy consumption predictiontemporal convolutional network (TCN)BiGRUattentiongolden jackal optimization |
| spellingShingle | Sijia Fu Rui Zhu Feiyang Yu Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention Applied Sciences energy consumption prediction temporal convolutional network (TCN) BiGRU attention golden jackal optimization |
| title | Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention |
| title_full | Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention |
| title_fullStr | Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention |
| title_full_unstemmed | Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention |
| title_short | Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention |
| title_sort | research on predictive analysis method of building energy consumption based on tcn bigru attention |
| topic | energy consumption prediction temporal convolutional network (TCN) BiGRU attention golden jackal optimization |
| url | https://www.mdpi.com/2076-3417/14/20/9373 |
| work_keys_str_mv | AT sijiafu researchonpredictiveanalysismethodofbuildingenergyconsumptionbasedontcnbigruattention AT ruizhu researchonpredictiveanalysismethodofbuildingenergyconsumptionbasedontcnbigruattention AT feiyangyu researchonpredictiveanalysismethodofbuildingenergyconsumptionbasedontcnbigruattention |