Method for predicting online learning photovoltaic power of leaky integral echo state networks

A technology of echo state network and power prediction, applied in the direction of neural learning method, prediction, biological neural network model, etc., can solve the problems of randomness, susceptibility to meteorological factors, etc., and achieve the effect of enhancing recognition ability

Inactive Publication Date: 2018-03-06
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

The daily photovoltaic power generation shows a certain regularity, but it is also easily affected by meteorological factors, and there is randomness

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  • Method for predicting online learning photovoltaic power of leaky integral echo state networks
  • Method for predicting online learning photovoltaic power of leaky integral echo state networks
  • Method for predicting online learning photovoltaic power of leaky integral echo state networks

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Embodiment Construction

[0059] Introduce the leaky integral neuron in the echo state network (ESN), analyze the influence of the parameters of the leaky integral echo state network (LIESN) on its photovoltaic power prediction performance, and obtain the optimized photovoltaic power prediction model; use least squares The online learning algorithm is used to train the model, and the online learning leakage integral type echo state network prediction model is obtained, and finally the online learning photovoltaic power prediction based on the leakage integral type echo state network is realized.

[0060] The online learning photovoltaic power prediction based on LIESN mainly includes the following steps.

[0061] Step 1 introduces a leaky integration neuron:

[0062] The sigmoid neuron used in the echo state network has no memory, and its state value at time n+1 has no direct relationship with the state value at time n. Therefore, the echo state network is more suitable for discrete system modeling pr...

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Abstract

The invention relates to photovoltaic power prediction, discloses a method for predicting online learning photovoltaic power of leaky integral echo state networks, and aims at providing a prediction model for analyzing influences, on photovoltaic power prediction performance, of parameters of leaky integral echo state networks (LIESN) and then obtaining photovoltaic power. A least squares online learning algorithm is utilized to train the model so as to obtain an online learning leaky integral echo state network prediction model and then finally realize leaky integral echo state network-basedonline learning photovoltaic power prediction. The method for predicting online learning photovoltaic power of leaky integral echo state networks comprises the following steps of: 1, importing a leakyintegral nerve cell; 2, setting parameters; 3, carrying out training by utilizing the online learning algorithm; and 4, predicting photovoltaic output power. The method is mainly applied to the photovoltaic power prediction occasions.

Description

technical field [0001] The present invention relates to photovoltaic power forecasting, in particular to a method for online learning photovoltaic power forecasting of leakage integral type echo state network. Background technique [0002] Solar energy is a natural clean energy. With the rapid development and large-scale commercial application of photovoltaic power generation technology in recent years, photovoltaic power generation will become one of the most important power generation methods for human beings in the future. However, photovoltaic power generation is uncertain and intermittent, and is easily affected by weather and seasonal conditions; grid-connected photovoltaic power generation will also have an impact on the power system, which is not conducive to its stable operation. Therefore, the accurate prediction of photovoltaic power generation is beneficial to the dispatching of the power system, reduces operating costs, and maximizes the benefits of the entire s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
Inventor 路志英徐正阳李鑫
Owner TIANJIN UNIV
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