Short-time wind speed forecasting method based on neural network

A neural network and artificial neural network technology, applied in the field of short-term wind speed prediction, can solve problems such as inapplicable neural network weights and large deviations in prediction results

Inactive Publication Date: 2010-07-28
NORTHWEST CHINA GRID
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AI Technical Summary

Problems solved by technology

Solved the problems that the neural network weights gradually become inapplicable over time, and the prediction effect has large deviations

Method used

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  • Short-time wind speed forecasting method based on neural network
  • Short-time wind speed forecasting method based on neural network
  • Short-time wind speed forecasting method based on neural network

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Embodiment Construction

[0019] Data collection: The observation data of the NRG wind measuring tower is used as the sample data, and the observation data includes wind speed, wind direction, temperature and air pressure every 10 minutes. The wind measurement data is sorted into a continuous time series, which is filtered and preprocessed.

[0020] Value-added correction prediction: Due to the large variation of wind speed in space and time, direct prediction of wind speed will lead to great errors. In the current neural network wind speed prediction, the filtering wind speed increment prediction method is used. This method avoids directly predicting wind speed values, and instead makes corrections from incremental forecasts to generate actual predicted wind speeds.

[0021] Forecast basic assumptions

[0022] (1) It is assumed that there is a certain pseudo-period in the wind speed

[0023] (2) It is assumed that the wind speed increment also has a certain pseudo-period

[0024] (3) Assume that t...

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Abstract

The invention discloses a short-time wind speed forecasting method based on a neural network. The method comprises the following steps of: (1) recording the moving average observing values of the wind speed, wind direction, temperature and air pressure on the same district once at the interval of 10 minutes, and ordering the observed data in a time sequence from front to back to obtain original wind speed data; (2) calculating the data of the adjacent time according to the time sequence, and generating an original wind speed added value sequence; (3) entering the original airspeed added value into a BP artificial neural network to construct a wind speed added value neural network model, calculating and counting an original wind speed added value tendency by utilizing the BP artificial neural network, training a RP artificial neural network by respectively utilizing the original wind speed added value and the original wind speed added value tendency as the original data, and obtaining a wind speed added value predicting value and a wind speed added value error tendency; (4) adding the wind speed added value predicting value into statistical noise to reduce and generate a wind speed predicting value; (5) carrying out moving filtering on the wind speed predicting value; and (6) obtaining the wind speed predicting value 4 hours in advance.

Description

Technical field: [0001] The invention relates to the field of short-term wind speed prediction, in particular to the prediction of short-time (24 hours) wind speed by combining artificial neural network and time series method. Background technique [0002] Wind power generation is a clean, non-polluting renewable energy generation method. Vigorously developing renewable energy is an important part of my country's energy development strategy. In recent years, wind energy, as a large-scale commercially developed renewable energy, has developed extremely rapidly. Wind power has the characteristics of intermittency and volatility, which have a great impact on the safe and stable operation of wind power connected to the grid. Especially in recent years, with the rapid development of my country's wind power industry, it is even more urgent to effectively solve this problem. Experience at home and abroad has proved that forecasting the output of wind power can greatly reduce the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01W1/10G01P5/00G06N3/02
Inventor 姜宁朱敏奕魏磊高媛媛孙川永于广亮
Owner NORTHWEST CHINA GRID
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