View source for 20230136352. METHOD AND SYSTEM FOR PREDICTING A DAY-AHEAD WIND POWER OF WIND FARMS simplified abstract (Economic and Technological Research Institute of State Grid Liaoning Electric Power Co., Ltd.)

=METHOD AND SYSTEM FOR PREDICTING A DAY-AHEAD WIND POWER OF WIND FARMS=


==Organization Name==

[[:Category:Economic and Technological Research Institute of State Grid Liaoning Electric Power Co., Ltd.|Economic and Technological Research Institute of State Grid Liaoning Electric Power Co., Ltd.]]


[[Category:Economic and Technological Research Institute of State Grid Liaoning Electric Power Co., Ltd.]]

==Inventor(s)==

[[:Category:XIAO Pan of Shenyang (CN)|XIAO Pan of Shenyang (CN)]][[Category:XIAO Pan of Shenyang (CN)]]

[[:Category:MINGLI Zhang of Shenyang (CN)|MINGLI Zhang of Shenyang (CN)]][[Category:MINGLI Zhang of Shenyang (CN)]]

[[:Category:LIN Zhao of Shenyang (CN)|LIN Zhao of Shenyang (CN)]][[Category:LIN Zhao of Shenyang (CN)]]

[[:Category:NA Zhang of Shenyang (CN)|NA Zhang of Shenyang (CN)]][[Category:NA Zhang of Shenyang (CN)]]

[[:Category:ZHUORAN Song of Shenyang (CN)|ZHUORAN Song of Shenyang (CN)]][[Category:ZHUORAN Song of Shenyang (CN)]]

[[:Category:NANTIAN Huang of Jilin (CN)|NANTIAN Huang of Jilin (CN)]][[Category:NANTIAN Huang of Jilin (CN)]]

[[:Category:JING Gao of Shenyang (CN)|JING Gao of Shenyang (CN)]][[Category:JING Gao of Shenyang (CN)]]

[[:Category:XUMING Lv of Shenyang (CN)|XUMING Lv of Shenyang (CN)]][[Category:XUMING Lv of Shenyang (CN)]]

[[:Category:HUA Li of Shenyang (CN)|HUA Li of Shenyang (CN)]][[Category:HUA Li of Shenyang (CN)]]

[[:Category:MENGZENG Cheng of Shenyang (CN)|MENGZENG Cheng of Shenyang (CN)]][[Category:MENGZENG Cheng of Shenyang (CN)]]

[[:Category:XING Ji of Shenyang (CN)|XING Ji of Shenyang (CN)]][[Category:XING Ji of Shenyang (CN)]]

[[:Category:WENYING Shang of Shenyang (CN)|WENYING Shang of Shenyang (CN)]][[Category:WENYING Shang of Shenyang (CN)]]

[[:Category:YIXIN Hou of Shenyang (CN)|YIXIN Hou of Shenyang (CN)]][[Category:YIXIN Hou of Shenyang (CN)]]

[[:Category:SUO Yang of Shenyang (CN)|SUO Yang of Shenyang (CN)]][[Category:SUO Yang of Shenyang (CN)]]

[[:Category:BO Yang of Shenyang (CN)|BO Yang of Shenyang (CN)]][[Category:BO Yang of Shenyang (CN)]]

[[:Category:YUTONG Liu of Shenyang (CN)|YUTONG Liu of Shenyang (CN)]][[Category:YUTONG Liu of Shenyang (CN)]]

[[:Category:LINKUN Man of Shenyang (CN)|LINKUN Man of Shenyang (CN)]][[Category:LINKUN Man of Shenyang (CN)]]

[[:Category:XILIN Xu of Shenyang (CN)|XILIN Xu of Shenyang (CN)]][[Category:XILIN Xu of Shenyang (CN)]]

[[:Category:HAIFENG Yang of Shenyang (CN)|HAIFENG Yang of Shenyang (CN)]][[Category:HAIFENG Yang of Shenyang (CN)]]

[[:Category:FANGYUAN Yang of Shenyang (CN)|FANGYUAN Yang of Shenyang (CN)]][[Category:FANGYUAN Yang of Shenyang (CN)]]

[[:Category:KAI Liu of Shenyang (CN)|KAI Liu of Shenyang (CN)]][[Category:KAI Liu of Shenyang (CN)]]

[[:Category:JINQI Li of Shenyang (CN)|JINQI Li of Shenyang (CN)]][[Category:JINQI Li of Shenyang (CN)]]

[[:Category:ZONGYUAN Wang of Shenyang (CN)|ZONGYUAN Wang of Shenyang (CN)]][[Category:ZONGYUAN Wang of Shenyang (CN)]]

==METHOD AND SYSTEM FOR PREDICTING A DAY-AHEAD WIND POWER OF WIND FARMS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230136352 titled 'METHOD AND SYSTEM FOR PREDICTING A DAY-AHEAD WIND POWER OF WIND FARMS

==Simplified Explanation==

The abstract describes a method for predicting the day-ahead wind power of wind farms. Here is a simplified explanation of the abstract:

* The method involves constructing a raw data set based on the correlation between the to-be-predicted daily wind power, numerical weather forecast meteorological features, and historical daily wind power.
* The raw data set is then clustered using k-means clustering, resulting in a data set with cluster labels.
* Massive labeled scenes are generated using robust auxiliary classifier generative adversarial networks based on the cluster labels.
* The cluster label category of the to-be-predicted day is determined based on known historical daily wind power and numerical weather forecast meteorological features.
* Multiple scenes with high similarity to the to-be-predicted daily wind power are screened out based on the cluster label category.
* Prediction results of the to-be-predicted daily wind power are obtained at multiple set times using the average value, upper limit value, and lower limit value of the to-be-predicted daily wind power.

Potential applications of this technology:

* Renewable energy management: The method can be used to predict the day-ahead wind power of wind farms, helping in the efficient management and utilization of renewable energy resources.
* Power grid planning: Accurate wind power predictions can assist in planning the power grid and optimizing the integration of wind energy into the existing infrastructure.

Problems solved by this technology:

* Uncertainty in wind power generation: By utilizing historical wind power data and numerical weather forecast meteorological features, this method addresses the challenge of predicting wind power generation accurately.
* Resource optimization: The method helps in optimizing the utilization of wind energy resources by providing reliable day-ahead predictions.

Benefits of this technology:

* Improved energy management: Accurate wind power predictions enable better planning and management of renewable energy resources, leading to more efficient energy generation and utilization.
* Cost savings: By optimizing the utilization of wind energy resources, this method can help reduce costs associated with energy generation and grid management.
* Enhanced grid stability: Reliable wind power predictions contribute to the stability and reliability of the power grid by facilitating better integration of intermittent renewable energy sources.


==Original Abstract Submitted==

a method for predicting a day-ahead wind power of wind farms, comprising: constructing a raw data set based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature and a historical daily wind power; obtaining a clustered data set and performing k-means clustering, obtaining a raw data set with cluster labels, and generating massive labeled scenes based on robust auxiliary classifier generative adversarial networks; determining the cluster label category of the to-be-predicted day based on the known historical daily wind power and numerical weather forecast meteorological feature, and screening out multiple scenes with high similarity to the to-be-predicted daily wind power based on the cluster label category; and obtaining the prediction results of the to-be-predicted daily wind power at a plurality of set times based on an average value, an upper limit value and a lower limit value of the to-be-predicted daily wind power.



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