Studies from Macau University of Science & Technology Reveal New Findings on Insurance (Wpfsad: Wind Power Forecasting System Integrating Dual-stage Attention and Deep Learning): Insurance

2024 APR 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Insurance. According to news originating from Macau, People’s Republic of China, by NewsRx correspondents, research stated, “Wind power forecasting with high accuracy and stability concretely contributes to efficient scheduling and risk management of wind power systems. However, current studies remain limited due to these two shortcomings: 1) overemphasizing model combination strategy and data preprocessing, thus ignoring the improvement of the model ‘ s internal computing mechanism; and 2) much attention was paid to the single-stage attention mechanism, but little is known about dual-stage attention (DSA) mechanism.”

Funders for this research include China Postdoctoral Science Foundation, National Natural Science Foundation of China (NSFC), Key R&D and Promotion Project in Henan Province.

Our news journalists obtained a quote from the research from the Macau University of Science & Technology, “In this article, we propose a wind power forecasting system (WPFSAD), especially attaching importance to the improvement of forecasting precision and stability using a DSA-based mechanism. Specifically, the features of the input and hidden layers are adaptively and dynamically extracted in-depth. Wind power datasets from three wind farms are used to carry out simulation experiments to demonstrate the effectiveness of the WPFSAD.”

According to the news editors, the research concluded: “The study findings confirm the total supremacy of the WPFSAD in forecasting precision and stability, convergence, and robustness over its benchmarks.”

This research has been peer-reviewed.

For more information on this research see: Wpfsad: Wind Power Forecasting System Integrating Dual-stage Attention and Deep Learning. IEEE Transactions on Industrial Informatics, 2023;19(11):11252-11264. IEEE Transactions on Industrial Informatics can be contacted at: Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Industrial Informatics - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424)

The news correspondents report that additional information may be obtained from Jianzhou Wang, Macau University of Science & Technology, Institute of Systems Engineering, Macau 999078, People’s Republic of China. Additional authors for this research include Tong Niu, Pei Du and Wendong Yang.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TII.2023.3245196. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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