Photovoltaic field station generated power prediction method and system

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...

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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. Therefore, photovoltaic power generation The system is not a continuous and stable power supply system, its output power has discontinuity and uncertainty, and the existing forecast of photovoltaic power generation does not take into account the impact of random fluctuations in power generation on the prediction results

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