18271966. PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM simplified abstract (BOE TECHNOLOGY GROUP CO., LTD.)
Contents
- 1 PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM
Organization Name
BOE TECHNOLOGY GROUP CO., LTD.
Inventor(s)
PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18271966 titled 'PREDICTION METHOD OF WIND POWER OUTPUT, ELECTRONIC DEVICE, STORAGE MEDIUM, AND SYSTEM
Simplified Explanation
The present disclosure provides a prediction method of wind power output, an electronic device, a storage medium, and a system, and relates to the technical field of wind power. The method includes: periodically acquiring an initial meteorological data set corresponding to each received time node, wherein the initial meteorological data set includes initial meteorological sub-data of at least one dimension of at least one meteorological element; after acquiring the latest initial meteorological data set, identifying and smoothing the abnormal sub-data to obtain a smoothed meteorological data set; determining an average wind energy density in a target time period; taking the smooth meteorological data set and the average wind energy density in the target time period as the input features of the model, and obtaining a wind power output predictive value via the model.
- Prediction method of wind power output
- Periodically acquiring initial meteorological data set
- Identifying and smoothing abnormal sub-data
- Determining average wind energy density
- Obtaining wind power output predictive value
Potential Applications
This technology can be applied in:
- Renewable energy forecasting
- Energy grid management
- Weather-dependent industries
Problems Solved
This technology helps in:
- Improving accuracy of wind power output predictions
- Enhancing efficiency of wind energy utilization
- Optimizing energy production planning
Benefits
The benefits of this technology include:
- Cost savings in energy production
- Reduction of reliance on non-renewable energy sources
- Minimization of environmental impact
Potential Commercial Applications
Commercial applications of this technology may include:
- Energy companies
- Weather forecasting services
- Renewable energy developers
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms for weather forecasting and energy production optimization.
Unanswered Questions
How does this technology compare to traditional methods of wind power output prediction?
This article does not provide a direct comparison between this technology and traditional methods of wind power output prediction.
What are the limitations of this prediction method in terms of accuracy and reliability?
This article does not address the potential limitations of this prediction method in terms of accuracy and reliability.
Original Abstract Submitted
The present disclosure provides a prediction method of wind power output, an electronic device, a storage medium and a system, and relates to the technical field of wind power. The method includes: periodically acquiring an initial meteorological data set corresponding to each received time node, wherein the initial meteorological data set includes initial meteorological sub-data of at least one dimension of at least one meteorological element; after acquiring the latest initial meteorological data set, identifying and smoothing the abnormal sub-data to obtain a smoothed meteorological data set; determining an average wind energy density in a target time period; taking the smooth meteorological data set and the average wind energy density in the target time period as the input features of the model, and obtaining a wind power output predictive value via the model.
