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Photovoltaic interval prediction method and system based on self-coding and extreme learning machine

A technology of extreme learning machine and prediction method, which is applied in prediction, system integration technology, information technology support system, etc. to achieve the effect of improving solution performance, ensuring accuracy, and reducing overfitting.

Inactive Publication Date: 2019-07-26
ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY +2
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, solar photovoltaic power generation is affected by the intensity of solar radiation, and its output has strong randomness and intermittency. After a large number of photovoltaics are connected to the distribution network as a distributed power source, they will affect the peak regulation, frequency regulation, backup, power flow, and bus voltage of the power grid. etc. have great influence

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  • Photovoltaic interval prediction method and system based on self-coding and extreme learning machine
  • Photovoltaic interval prediction method and system based on self-coding and extreme learning machine
  • Photovoltaic interval prediction method and system based on self-coding and extreme learning machine

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Embodiment 1

[0048] This embodiment provides a photovoltaic interval prediction method based on self-encoding and extreme learning machine. The interval prediction method includes the following steps:

[0049] Step 1), preprocess the historical data of photovoltaic output;

[0050] Step 2) Obtain the initial value of the photovoltaic prediction interval based on linear regression interval estimation;

[0051] Step 3), using self-encoding technology to initialize the input weight matrix of the dual-output extreme learning machine;

[0052] Step 4), establish a photovoltaic interval prediction model based on the dual-output extreme learning machine, and use the PSO heuristic algorithm to optimize and train the photovoltaic interval prediction model for the interval initial values ​​and input weight matrix obtained in steps 2) and 3).

[0053] The accuracy metrics for the PV forecast interval include the average forecast interval width PI mean , Prediction interval coverage rate PICP and comprehensive ...

Embodiment 2

[0077] This embodiment provides a photovoltaic interval prediction system based on self-encoding and extreme learning machine, which includes:

[0078] Data preprocessing unit: preprocess historical data of photovoltaic output;

[0079] The unit for obtaining the initial value of the photovoltaic prediction interval: based on the linear regression interval estimation, obtain the initial value of the photovoltaic prediction interval;

[0080] Input weight matrix initialization unit: use self-encoding technology to initialize the input weight matrix of the dual-output extreme learning machine;

[0081] Prediction model establishment unit: establish a photovoltaic interval prediction model based on dual-output extreme learning machine;

[0082] Prediction model optimization training unit: According to the interval initial value and input weight matrix obtained by the foregoing unit, the PSO heuristic algorithm is used to optimize the training of the photovoltaic interval prediction model. ...

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Abstract

The invention discloses a photovoltaic interval prediction method and system based on self-coding and an extreme learning machine. A dual-output extreme learning machine model is used for constructinga photovoltaic interval prediction model, dual outputs are the upper limit and the lower limit of a photovoltaic interval respectively, and due to lack of actual values of the photovoltaic interval,a traditional extreme learning machine training algorithm fails accordingly. According to the method, a heuristic algorithm is adopted to optimize a training extreme learning machine, a correspondinginitialization algorithm is provided for improving the calculation result and efficiency of the heuristic algorithm, and the method comprises: carrying out prediction interval initialization based onlinear regression interval estimation; carrying out inputting weight matrix initialization of an extreme learning machine based on self-coding; and carrying out optimization training of a particle swarm-based double-output extreme learning machine. The photovoltaic prediction interval obtained through the model replaces a traditional photovoltaic point prediction value, and more sufficient information can be provided for day-ahead robust scheduling and intra-day economic scheduling of a power system.

Description

Technical field [0001] The invention belongs to the field of distribution network scheduling optimization, and specifically is a photovoltaic interval prediction method and system based on a self-encoding and extreme learning machine. Background technique [0002] As a clean, renewable, and huge new energy source, solar photovoltaic power generation is gradually gaining popularity after wind power. However, solar photovoltaic power generation is affected by the intensity of solar radiation, and its output is highly random and intermittent. After a large number of photovoltaics are connected to the distribution network as distributed power sources, the peak regulation, frequency modulation, backup, power flow, and bus voltage of the grid Etc. have a greater impact. [0003] The interval forecast of photovoltaic power generation output is the basis for power system dispatch. Through accurate photovoltaic interval prediction, sufficient photovoltaic output forecast information can b...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/00
CPCG06N3/006G06Q10/04G06Q50/06Y02E40/70Y04S10/50
Inventor 李鹏张雪松龙寰汪科葛晓慧马瑜涵
Owner ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY
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