Short-term wind power forecasting method based on improved afsa and optimized elm

A technology for wind power prediction and wind speed, applied in the direction of prediction, electrical components, circuit devices, etc., can solve problems such as easy to fall into local minimum, increase calculation time cost, low prediction accuracy, etc., to achieve favorable prediction data and error probability Concentrated distribution and high prediction accuracy
CN106786499BActive Publication Date: 2019-07-02NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIV OF INFORMATION SCI & TECH
Publication Date
2019-07-02

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Abstract

The present invention discloses a short-period wind power prediction method for optimizing an ELM based on an improved AFSA (Artificial Fish Swarm Algorithm). The method comprises the following steps: randomly generating an artificial fish initial group, taking the weight and the threshold value of an extreme learning machine (ELM) network as the position vector of the artificial fish school, determining the food concentration of the artificial fish school through the root-mean-square error function of the ELM, solving the optimal position vector through adoption of an improved fish school method, and taking the position vector as the initial weight and the threshold value of the ELM network to perform prediction of the short-period wind power. The short-period wind power prediction method is fast in convergence rate, high in generalization ability and high in prediction accuracy, and has a good application prospect.
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Description

technical field

[0001] The invention relates to the technical field of short-term wind power forecasting, in particular to a short-term wind power forecasting method based on improved AFSA optimized ELM. Background technique

[0002] With the global warming and the depletion of non-renewable resources such as oil and natural gas, the development and utilization of clean and renewable energy has been increasingly attached great importance to by countries all over the world. Since the early 1990s in foreign countries, the research on short-term wind power forecasting methods has started. Due to the late start of wind power forecasting, there are still many deficiencies in forecasting accuracy, reliability and adaptability to different wind fields.

[0003] The extreme learning machine ELM is a new learning method in the single hidden layer feedforward neural network SLFN. The training process does not need to be adjusted, and the optimal solution can be obtained by setting the...

Claims

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