Radiation attenuation-considered photovoltaic power prediction method

Abstract

The invention relates to the technical field of photovoltaic power generation, and in particular to a radiation attenuation-considered photovoltaic power short-term prediction method. The method comprises the following steps of: carrying out training by adoption of an indirect prediction method so as to obtain a sunny day surface radiation prediction model; obtaining an attenuation coefficient ofhistory daily surface radiation, establishing a surface radiation attenuation coefficient prediction model according to the attenuation coefficient, and establishing a cloud coverage coefficient prediction model; carrying out training by taking history real surface radiation, temperature and humidity as inputs of a meteorological factor and taking photovoltaic power as an output, so as to obtain aphotovoltaic power prediction model; and predicting photovoltaic power generation power by utilizing the photovoltaic power prediction model by taking a predicted value of the surface radiation as input of a surface radiation value and taking meteorological data of weather forecast as input of the meteorological factor. According to the method, the influence degree of cloud on the prediction precision can be reduced, the process of image analysis is saved and the algorithm is simpler and more efficient.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power prediction method considering radiation attenuation. Background technique [0002] Due to the influence of weather and other factors, the power curve of photovoltaic power generation has certain randomness and volatility. With the integration of large-scale photovoltaics into the grid, the uncertainty of its power generation has caused a huge impact on the safe and stable operation of the grid. Accurate photovoltaic power forecasting can provide effective support for grid dispatching and ensure the security and stability of the grid. [0003] In recent years, there have been methods for photovoltaic power prediction using numerical weather prediction combined with machine learning algorithms such as support vector machines and neural networks. However, because the solar radiation on the surface is affected by the movement and changes o...

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Problems solved by technology

However, in the method of using satellite cloud images and weather forecast information, the obtained cloud cover information covers a large area and cannot reflect the cloud occlusion information in the area where the photovoltaic power station is located. Therefore, it is suitable for short-term power prediction, but not for ultra-short-term power prediction.

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