Application and challenges of big data analytics in low-carbon indoor space design
The techniques of big data analysis hold immense potential in optimizing indoor energy consumption and enhancing comfort levels. This paper proposes a predictive method for effectively forecasting energy usage in libraries through a multi-step ahead time series-based long short-term memory-backpropa...
| Published in: | INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
OXFORD UNIV PRESS
2025
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| Subjects: | |
| Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001416683900001 |
| Summary: | The techniques of big data analysis hold immense potential in optimizing indoor energy consumption and enhancing comfort levels. This paper proposes a predictive method for effectively forecasting energy usage in libraries through a multi-step ahead time series-based long short-term memory-backpropagation model, integrated with building energy consumption sub-metering analysis technology. Experimental results indicate that the proposed multi-input multi-output model significantly outperforms traditional recursive and direct models in terms of predictive performance, adeptly capturing the intricate characteristics and temporal dependencies of energy consumption data, thereby offering a novel technological pathway and practical implications for building energy management. |
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| ISSN: | 1748-1317 1748-1325 |
| DOI: | 10.1093/ijlct/ctaf005 |