Photovoltaic field station generated power prediction method and system

A technology of power generation and forecasting methods, applied in forecasting, information technology support systems, instruments, etc., can solve problems such as uncertainty, discontinuity of output power, and failure to consider the impact of random fluctuations in power generation

Pending Publication Date: 2020-10-23
SHANDONG UNIV +2
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Problems solved by technology

[0005] The inventors found that the photovoltaic power generation depends on the intensity and angle of sunlight irradiation. When the solar radiation intensity is high, the photovoltaic power generation power fluctuates greatly, while when the solar radiation intensity is low, the random fluctuation range is small. Th

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  • Photovoltaic field station generated power prediction method and system
  • Photovoltaic field station generated power prediction method and system
  • Photovoltaic field station generated power prediction method and system

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Embodiment 1

[0035] In this embodiment, integrated learning is applied to the power generation prediction of photovoltaic plants and stations. Integrated learning is a combination method, and the performance is better than that of a single learner by combining a variety of different learners; Then combine them, and then train the integrated model, usually using existing learning algorithms, such as decision trees, neural networks, support vector machines, etc., to generate independent learners from the training data. Compared with a single model, integrated learning can fully mine the information in training samples, so as to obtain more accurate and reliable prediction results.

[0036] like figure 1 As shown, this embodiment provides a method for predicting power generation of a photovoltaic field station, including:

[0037] S1: Construct a training sample set according to the obtained historical power data of the photovoltaic station and the meteorological data of the corresponding ti...

Embodiment 2

[0079] This embodiment provides a photovoltaic power generation power prediction system, including:

[0080] The weight allocation module is used to construct a training sample set according to the acquired historical power data of the photovoltaic station and the meteorological data of the corresponding time period, and allocate a sample weight to each training sample in the training sample set;

[0081] The training module is used to train the random forest model using the sub-training sample set generated by the self-service sampling method for the training sample set, and calculate the error rate and weight coefficient of the random forest model under the current sample weight according to the adaptive enhancement algorithm;

[0082] The iteration module is used to update the sample weight according to the error rate and the weight coefficient under the preset number of iterations, train the random forest model in turn, and weight it according to the weight coefficient to o...

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Abstract

The invention discloses a photovoltaic station generated power prediction method and system. The method comprises the steps: building a training sample set according to the historical power data of aphotovoltaic station and the meteorological data of a corresponding time period, and distributing a sample weight to each training sample in the training sample set; training a random forest model byadopting a sub-training sample set generated from the training sample set through a self-service sampling method, and calculating an error rate and a weight coefficient of the random forest model under the current sample weight according to an adaptive enhancement algorithm; under a preset iteration frequency, updating the sample weight according to the error rate and the weight coefficient, sequentially training random forest models, and weighting the random forest models according to the weight coefficient to obtain a weighted random forest prediction model; and predicting the meteorologicaldata in the to-be-predicted time period by adopting the weighted random forest prediction model to obtain the power generation power of the photovoltaic station. Information in multi-dimensional features is fully mined, the uncertainty problem existing in photovoltaic power generation is solved, and the reliability and accuracy of photovoltaic station power prediction are improved.

Description

technical field [0001] The present invention relates to the technical field of photovoltaic power generation, in particular to a method and system for predicting power generation of a photovoltaic field station. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] At present, the prediction methods of photovoltaic power generation can be divided into three categories: physical methods, statistical methods and combined methods. For the physical method, in addition to considering solar radiation, other influencing factors are usually taken into account when the model is established, such as the angle of incidence of the sun, the degree of pollution on the surface of the photovoltaic panel, the degree of aging, and the temperature of the battery. Prediction using physical methods does not require a large amount of historical data, so it is suitab...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N20/20
CPCG06Q10/04G06Q50/06G06N3/006G06N20/20Y04S10/50Y02A30/00
Inventor 杨明王冠杰于一潇朱长胜蒿峰
Owner SHANDONG UNIV
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