2022 MAY 12 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Investigators publish new report on Energy - Wind Farms. According to news reporting out of Shenyang, People’s Republic of China, by NewsRx editors, research stated, “In the stochastic optimal scheduling of microgrid with multiple wind farms, the accurate description of uncertainties is a critical issue. Scenario generation provides an effective way to represent the strong randomness and interdependence between wind speeds.”
Financial supporters for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities.
Our news journalists obtained a quote from the research from Northeastern University, “However, there may be very limited data or no historical information in the beginning stage of a newly-built wind farm operation, which will lead to the inaccuracy of scenario generation and thus affect the reliability of decision results. In this paper, considering that multiple wind farms in the adjacent areas may have similar weather conditions, a novel transfer learning-based scenario generation method is proposed to utilize the historical information from other existing data-rich farms for generating wind speed scenarios of the new farm. The scenario generation tasks are constructed as a cross-domain adaption problem. To model the target wind speed, joint distribution adaption (JDA) is adopted to explore the underlying relationship between multiple source farms and the target farm.”
According to the news editors, the research concluded: “Experimental results show that the scenarios generated by our proposed method can better describe the properties of target wind speed, and the microgrid scheduling results can be more reliable in the case of very limited data.(.”
This research has been peer-reviewed.
For more information on this research see: A Transfer Learning-based Scenario Generation Method for Stochastic Optimal Scheduling of Microgrid With Newly-built Wind Farm. Renewable Energy, 2022;185:1139-1151. Renewable Energy can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Renewable Energy - http://www.journals.elsevier.com/renewable-energy/)
Our news journalists report that additional information may be obtained by contacting Hongru Li, Northeastern University, College of Information Science and Engineering, Shenyang 110819, People’s Republic of China.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.renene.2021.12.110. 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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