Predicting indoor temperature distribution with low data dependency using recurrent neural networks
Accurately predicting indoor temperature distribution can provide valuable reference data, helping residents independently adjust HVAC equipment around them to ensure comfort while reducing unnecessary energy consumption. This study proposes a prediction framework composed of two neural networks, en...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-03-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2474818 |
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| _version_ | 1849737556024885248 |
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| author | Jiahe Wang Shohei Miyata Keiichiro Taniguchi Yasunori Akashi |
| author_facet | Jiahe Wang Shohei Miyata Keiichiro Taniguchi Yasunori Akashi |
| author_sort | Jiahe Wang |
| collection | DOAJ |
| description | Accurately predicting indoor temperature distribution can provide valuable reference data, helping residents independently adjust HVAC equipment around them to ensure comfort while reducing unnecessary energy consumption. This study proposes a prediction framework composed of two neural networks, enabling accurate indoor temperature distribution prediction with minimal training data in both temporal and spatial dimensions. The Dual-Stage Attention-Based Recurrent Neural Network calculates the importance ranking of feature values to enhance individual feature information and reduce training data volume, while Long Short-Term Memory is used to predict time-series features. From February to September 2022, 22 temperature sensors were installed in a target office to collect minute-by-minute indoor temperature data, which served as training and testing datasets. The results showed that, for short-term prediction, using data from five out of the 22 sensors collected over two weeks in winter (heating season) and summer (cooling season), the framework accurately predicted temperatures at the remaining 17 sensor locations, with a root mean squared error between 0.3 and 0.7. This study is significant for continuous indoor temperature prediction under low data volume conditions. |
| format | Article |
| id | doaj-art-41871bf688db40ab8a0227aa992aeb91 |
| institution | DOAJ |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-41871bf688db40ab8a0227aa992aeb912025-08-20T03:06:53ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-03-010011410.1080/13467581.2025.24748182474818Predicting indoor temperature distribution with low data dependency using recurrent neural networksJiahe Wang0Shohei Miyata1Keiichiro Taniguchi2Yasunori Akashi3The University of TokyoThe University of TokyoThe University of TokyoThe University of TokyoAccurately predicting indoor temperature distribution can provide valuable reference data, helping residents independently adjust HVAC equipment around them to ensure comfort while reducing unnecessary energy consumption. This study proposes a prediction framework composed of two neural networks, enabling accurate indoor temperature distribution prediction with minimal training data in both temporal and spatial dimensions. The Dual-Stage Attention-Based Recurrent Neural Network calculates the importance ranking of feature values to enhance individual feature information and reduce training data volume, while Long Short-Term Memory is used to predict time-series features. From February to September 2022, 22 temperature sensors were installed in a target office to collect minute-by-minute indoor temperature data, which served as training and testing datasets. The results showed that, for short-term prediction, using data from five out of the 22 sensors collected over two weeks in winter (heating season) and summer (cooling season), the framework accurately predicted temperatures at the remaining 17 sensor locations, with a root mean squared error between 0.3 and 0.7. This study is significant for continuous indoor temperature prediction under low data volume conditions.http://dx.doi.org/10.1080/13467581.2025.2474818indoor temperature predictiontemperature distributionlstmattention mechanismspatial measured data |
| spellingShingle | Jiahe Wang Shohei Miyata Keiichiro Taniguchi Yasunori Akashi Predicting indoor temperature distribution with low data dependency using recurrent neural networks Journal of Asian Architecture and Building Engineering indoor temperature prediction temperature distribution lstm attention mechanism spatial measured data |
| title | Predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| title_full | Predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| title_fullStr | Predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| title_full_unstemmed | Predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| title_short | Predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| title_sort | predicting indoor temperature distribution with low data dependency using recurrent neural networks |
| topic | indoor temperature prediction temperature distribution lstm attention mechanism spatial measured data |
| url | http://dx.doi.org/10.1080/13467581.2025.2474818 |
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