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1.
Sensors (Basel) ; 24(8)2024 Apr 15.
Artículo en Inglés | MEDLINE | ID: mdl-38676138

RESUMEN

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

2.
Bull Environ Contam Toxicol ; 84(1): 29-33, 2010 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-19820888

RESUMEN

Ambient measurement and microenvironmental modeling were compared with personal measurement in Beijing, China to evaluate their capacity to determine personal exposure to PM(2.5). The comparison showed the association was insignificant between ambient and personal concentrations, but was significant between modeled and personal concentrations. The association between ambient and personal concentrations was improved for non-smoking dormitories, on heavily polluted days and on weekdays. The median difference was 41% between ambient and personal concentrations and 17% between modeled and personal concentrations. The factors affecting the association and agreement between methods were indoor sources and ubiquitous "personal cloud".


Asunto(s)
Exposición a Riesgos Ambientales/análisis , Monitoreo del Ambiente/métodos , Contaminantes Ambientales/análisis , Material Particulado/análisis , China , Contaminantes Ambientales/química , Humanos , Tamaño de la Partícula , Material Particulado/química
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