Power prediction method for photovoltaic devices

Abstract

The invention relates to an output power prediction method for photovoltaic generating devices; the method comprises: predicting by using a physical model prediction, and a neural network prediction of environmental factors and a rolling a neural network prediction; setting the physical model prediction as P0, the neural network prediction of the environmental factors as P1, the rolling a neural network prediction as P2, and final power prediction output value as p; if P1 is more than P0 and P2 is more than P0, P is equal to P0; if the P1 is more than P0 that is more than P2, P is equal to P2; if the P2 is more than P0 that is more than P1, P is equal to P1; if P1 is less than P0 and P2 is less than P0, P is equal to (P1+P2)/2. According to the output power prediction method for photovoltaic generating devices provided by the invention, relatively complex mathematic relation is changed to be simple and reasonable mathematic relation, thus being beneficial to applying to actual engineering, improving predicting speed, reducing predicting difficulty, and being capable of significantly improving accuracy of prediction value of photovoltaic module output power, controlling error range and eliminating outlier.

Description

technical field [0001] The invention relates to a method for predicting output power of photovoltaic power generation equipment. Background technique [0002] As an important distributed power source, solar photovoltaic power generation is gradually being used by people from an independent system to a large-scale grid connection. However, since photovoltaic power generation is affected by solar radiation intensity, battery components, temperature, weather clouds and some random factors, the system operation process is an unbalanced random process, and its power generation and output power are highly random, fluctuate greatly and are not easy to control , especially when the weather changes abruptly. After this power generation method is connected to the grid, it will inevitably bring a series of problems to the security and management of the grid. Therefore, it is particularly important to predict the power generation efficiency of the photovoltaic system more accurately i...

AI Technical Summary

Problems solved by technology

[0004] However, the current neural network model can only control the prediction error to about 20%, and there is still a lot of power waste in power grid dispatching based on the prediction results

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