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Photovoltaic power generation system power predicting method of elman-based neural network

A technology of photovoltaic power generation system and neural network, applied in the field of power prediction of photovoltaic power generation system based on elman neural network, can solve the problems of information disappearance, instability, and inability to adapt to time-varying characteristics, etc., to achieve reduced volatility and high precision Effect

Inactive Publication Date: 2015-01-07
GUANGZHOU HKUST FOK YING TUNG RES INST
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AI Technical Summary

Problems solved by technology

[0006] (1) There is only feedforward without feedback, and the sensitivity to historical data is too poor, which will easily lead to the disappearance of the information of the learned learning mode, which is not stable enough;
[0007] (2) The ability to process dynamic information is too weak to directly and dynamically reflect the characteristics of the photovoltaic power generation system in the dynamic process, and it does not have the ability to adapt to time-varying characteristics, and the prediction accuracy fluctuates greatly

Method used

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  • Photovoltaic power generation system power predicting method of elman-based neural network
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  • Photovoltaic power generation system power predicting method of elman-based neural network

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no. 1 example

[0061] The present invention adopts the prediction model based on Elman neural network to realize the short-term prediction of photovoltaic system power generation, the structure of Elman neural network model is as follows figure 2 shown. Among them, X 1 , X 2 ···Xu is the node of the input layer, corresponding to the input forecast weather parameters and the power generation power of the photovoltaic system in the previous week; Y 1 is the node of the output layer, corresponding to the output forecast daily system power generation; l 1 , l 2 ···l N Is the node of the hidden layer, where the number n of hidden layer nodes (that is, the optimal number of neurons in the hidden layer) is determined by gradually increasing the trial method; C 1 , C 2 ···C N It is the node of the receiving layer, which is used to remember the output value of the hidden layer unit at the previous moment and return it to the input of the hidden layer.

[0062] The nonlinear state space expre...

no. 2 example

[0067] The main implementation steps of the photovoltaic power generation system power prediction method of the present invention are as follows:

[0068] Step 1, obtain the historical data of the power generation of photovoltaic power generation equipment in the relevant area, including the hourly power generation W and the effective power generation time period f, so as to obtain the effective power generation time period of the predicted power generation;

[0069] Step 2, obtain corresponding historical weather parameter information, including but not limited to temperature T, air pressure P, wind direction WD, wind speed WS, cloud cover C, rainfall R, sunshine time t and weather type P;

[0070] Step 3: Statistically obtain the historical data of generated power and historical weather parameter information, take the actual generated power of a day as the output data of the neural network, and use the hourly generated power W in the effective time period f of the previous we...

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Abstract

The invention discloses a photovoltaic power generation system power predicting method of an elman-based neural network. The method comprises the steps of obtaining generated power historical data and corresponding historical weather parameter information of photovoltaic power generation equipment at the related area, determining input data and output data of the neural network, determining the optimal number of hidden layer neurons, building the elman-based neural network accordingly, carrying out normalization processing on the generated power historical data and the historical weather parameter information, training the built neural network according to the data obtained after normalization processing is carried out, controlling prediction errors of the elman-based neural network to be within the preset range accordingly, regarding generated power historical data of one week before the prediction day and weather parameter data of the prediction day as input, and predicting the generated power of the prediction day through the trained neural network. The method has the advantages of being stable and high in time-varying adaptability and prediction precision, and can be widely applied to the field of photovoltaic power generation.

Description

technical field [0001] The invention relates to the field of photovoltaic power generation, in particular to a method for predicting the power of a photovoltaic power generation system based on an elman neural network. Background technique [0002] Renewable energy power generation is a relatively efficient and clean renewable energy utilization method, and it is also one of the most mature, large-scale development conditions and commercial development prospects among current renewable energy utilization technologies. Photovoltaic power generation is the main utilization method of renewable energy and is the main component of smart grid. The prediction of short-term power generation is the key to the successful promotion of photovoltaic power generation. It is also the basis for the power dispatching department to formulate power dispatching plans, and it is also an important guarantee for the benefits of self-built photovoltaic power generation systems such as households or...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
CPCG06N3/082G06Q50/06G06N3/044Y02A30/00Y04S10/50
Inventor 杨林吕洲高福荣姚科
Owner GUANGZHOU HKUST FOK YING TUNG RES INST
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