Zhejiang University

Changsha University of Science and Technology

Shenzhen Technology University

Jiayi Peng

Bin Li

Heng Zhang

Huixia Chen

Yunhao Yang

Lijuan Guo

Jieni He

Shurong Peng

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.

Forecast method and system of wind power probability density

Growing community of inventors

Changsha, China

Jiayi Peng