Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model
The results demonstrate that the LSTM-AT-DP model significantly enhances prediction accuracy while effectively suppressing error accumulation in long-term forecasts. This advancement provides robust technical support for precise facility environment regulation, with particular improvements observed in humidity prediction. The integrated attention mechanism proves particularly effective in identifying and weighting critical temporal features across all measured environmental parameters.
Authors
- Hui Shi (ORCID: https://orcid.org/0000-0002-6849-0793)
- Zhaoyuan Wang (ORCID: https://orcid.org/0000-0002-0092-5949)
- Shengjie Wang (ORCID: https://orcid.org/0000-0003-3990-5803)
- Chang-Hong Li (ORCID: https://orcid.org/0000-0003-3202-9302)
- Ming Diao (ORCID: https://orcid.org/0000-0003-1607-519X)
- Longwei Liang (ORCID: https://orcid.org/0009-0007-0098-6235)
Institutions
- Shihezi University (CN)
- Beijing Academy of Agricultural and Forestry Sciences (CN)
- National Engineering Research Center for Information Technology in Agriculture (CN)
- University of Almería (ES)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3389/fpls.2025.1652478
- Citations
- 3
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 2.40
Funders
- Shihezi University