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.

Date of Patent:
Sep. 24, 2024

Filed:

Jan. 14, 2022
Applicant:

State Grid Zhejiang Electric Power Co., Ltd. Taizhou Power Supply Company, Zhejiang, CN;

Inventors:

Jiandong Si, Zhejiang, CN;

Feng Guo, Zhejiang, CN;

Zhijian Yu, Zhejiang, CN;

Jiahao Zhou, Zhejiang, CN;

Lintong Wang, Zhejiang, CN;

Yefeng Luo, Zhejiang, CN;

Zhouhong Wang, Zhejiang, CN;

Dongbo Zhang, Zhejiang, CN;

Yuande Zheng, Zhejiang, CN;

Yuyin Qiu, Zhejiang, CN;

Jie Yu, Zhejiang, CN;

Zihuai Zheng, Zhejiang, CN;

Lei Hong, Zhejiang, CN;

Binren Wang, Zhejiang, CN;

Ying Ren, Zhejiang, CN;

Yuxi Tu, Zhejiang, CN;

Huili Xie, Zhejiang, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H02J 3/00 (2006.01); G06Q 10/04 (2023.01); G06Q 50/06 (2012.01);
U.S. Cl.
CPC ...
H02J 3/003 (2020.01); G06Q 10/04 (2013.01); G06Q 50/06 (2013.01);
Abstract

Disclosed are a net load forecasting method and apparatus for a new energy electric power market. The method includes: obtaining and performing data preprocessing on new energy output data and external environmental data, and extracting strongly correlated features from the new energy output data and the external environmental data after the data preprocessing; performing feature expansion on the strongly correlated features, and inputting the strongly correlated features after the feature expansion into a preconstructed regression forecasting model, to obtain a first forecast value; obtaining and performing data preprocessing on user load data and load influencing factor data, and inputting the user load data and the load influencing factor data after the data preprocessing into a FNN-LSTM hybrid model, to obtain a second forecast value; and calculating a difference between the second forecast value and the first forecast value, to obtain a net load forecasting result.


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