Neural network photovoltaic power generation prediction method and system suitable for small samples

Abstract

The invention discloses a neural network photovoltaic power generation prediction method and system suitable for small samples. The method comprises the steps that firstly, historical (small sample) photovoltaic power generation power data and meteorological data are acquired; Establishing a BP neural network photovoltaic power generation prediction model according to factors influencing photovoltaic power generation; In order to solve the problem of overfitting of a prediction model caused by too few training data, a Dropout strategy is adopted to optimize a neural network, and in order to solve the problem that a BP neural network is prone to being caught in a minimum value, a genetic algorithm is adopted to optimize the neural network. The sample data is divided into the training data set and the test data set, the training data is used for training the neural network photovoltaic power generation prediction model, the test data set is used for testing the network, and the generalization ability of the neural network photovoltaic power generation prediction model is improved. By adopting the method provided by the invention, the problem of low precision caused by over-fitting ofthe neural network photovoltaic power generation prediction model under the condition of small sample historical data can be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a neural network photovoltaic power generation prediction method and system suitable for small samples. Background technique [0002] Compared with traditional fossil energy, solar energy has more abundant resources, is almost inexhaustible, and is friendly to the environment. However, photovoltaic power generation is greatly affected by weather conditions and has the characteristics of intermittent and randomness. If the photovoltaic power generation cannot be accurately predicted, it will inevitably have a serious impact on the stable operation of the power grid when the photovoltaic power station is integrated into the large power grid. [0003] Neural network is widely used in photovoltaic power generation prediction because it can quickly and effectively establish the relationship between nonlinear input and output, and can fit almost all nonlinear relatio...

AI Technical Summary

Problems solved by technology

However, the traditional neural network algorithm often needs more than 2 months of sample data, and it is easy to fall into the situation of overfitting when the training samples are insufficient, and the trained simulation cannot be used

In the initial stage of substation operation, the historical sample data is insufficient, and the conventional artificial intelligence neural network cannot be applied to this situation

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