The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Sep. 16, 2025
Filed:
May. 09, 2022
Shenzhen Technology University, Shenzhen, CN;
Zhejiang University, Hangzhou, CN;
Changsha University of Science & Technology, Changsha, CN;
Shurong Peng, Changsha, CN;
Yunhao Yang, Changsha, CN;
Jiayi Peng, Changsha, CN;
Bin Li, Changsha, CN;
Heng Zhang, Changsha, CN;
Jieni He, Changsha, CN;
Lijuan Guo, Changsha, CN;
Huixia Chen, Changsha, CN;
SHENZHEN TECHNOLOGY UNIVERSITY, Shenzhen, CN;
ZHEJIANG UNIVERSITY, Hangzhou, CN;
CHANGSHA UNIVERSITY OF SCIENCE & TECHNOLOGY, Changsha, CN;
Abstract
A forecast method and system of wind power probability density. The forecast method includes: acquiring wind power data, preprocessing the wind power data, establishing a data set; then, constructing a time-variant deep feed-forward neural network forecast model, where the model includes multiple layers of neural networks, and each layer of neural network includes an input layer, a hidden layer and an output layer which are connected in sequence; taking wind power data at adjacent moments as an input of two input layers of two adjacent layers of neural networks, taking probability density distribution of wind power at adjacent moments as an output of two output layers of two adjacent layers of neural networks, and training and testing the model; inputting the wind power data to be forecasted into the trained time-variant deep feed-forward neural network forecast model for forecasting to obtain a more accurate and reliable wind power forecast result.