公开(公告)号：US20230037193A1

公开(公告)日：2023-02-02

申请号：US17774735

申请日：2021-08-03

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Jun ZHAO ,  Tianyu WANG ,  Wei WANG

IPC: H02J3/00 ,  H02J3/38 ,  H02J3/46 ,  G05B19/042

Abstract: The present invention belongs to the technical field of information, particularly relates to the theories such as time series interval prediction, extreme learning machine modeling and Gaussian approximation solution, and is a wind power output interval prediction method. First, interval prediction of wind power output influencing factors is realized by time series analysis and normal exponential smoothing so as to consider an input noise factor. Then an extreme learning machine prediction model is established with an interval result as an input, output distribution is calculated based on iterative expectation and a conditional variance law, and thus an interval prediction result of wind power output is obtained. The method has advantages in interval prediction performance and calculation efficiency and can provide guidance for production, scheduling and safe operation of a power system.

公开(公告)号：US20240411964A1

公开(公告)日：2024-12-12

申请号：US18699786

申请日：2021-10-26

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Tianyu WANG ,  Jun ZHAO ,  Wei WANG

IPC: G06F30/27

Abstract: A long-term scheduling method for an industrial byproduct gas system comprises the following steps: dividing information granularity according to the fluctuation features of energy data to form semantic representation of a data sample; with granular data features as input, constructing a deep contrastive network structure through expert scheduling experience data, and constructing knowledge representation under different scheduling states in modes of qualitative and quantitative learning; establishing a fully connected output layer to fit expert scheduling amount, to obtain an initial scheduling policy based on experience knowledge; constructing an actor-critic architecture to calculate a compensation policy that considers long-term scheduling performance.