Photovoltaic power generation power prediction method based on deep belief network

Abstract

The invention provides a photovoltaic power generation power prediction method based on a deep belief network. The photovoltaic power generation power prediction method comprises the following steps of collecting historical meteorological data and photovoltaic power generation output data of a to-be-predicted region to construct a training data set; dividing the historical meteorological data intoa plurality of samples, preprocessing the samples and constructing a meteorological factor data matrix; clustering the meteorological factor data matrix by adopting a fuzzy C-means clustering algorithm; establishing a photovoltaic power generation power prediction model by using a deep belief network; optimizing the initial weight of the prediction model by adopting a particle swarm algorithm; inputting historical meteorological data in the training data set into the prediction model for training to obtain the optimal weight of each layer of the prediction model, and storing the optimal weight; and judging the meteorological factor category of the to-be-predicted day by adopting a fuzzy C-means clustering algorithm, and then inputting all samples, which belong to the same category as theto-be-predicted day, in the meteorological factor data matrix into the prediction model to obtain predicted photovoltaic power generation power.

Description

technical field [0001] The present invention relates to the technical field of electric power system and automation thereof, and more specifically, relates to a photovoltaic power prediction method based on a deep belief network. Background technique [0002] Due to uncertain factors such as randomness, volatility, and intermittency of photovoltaic power generation, when large-scale photovoltaic power plants are connected to the grid, it will bring a series of problems to the safe and stable operation of the power system, such as voltage and frequency deviations, voltage Volatility and disconnection. Therefore, accurate prediction of photovoltaic power generation is conducive to large-scale photovoltaic power generation grid integration and reduces the harm caused by photovoltaic power generation grid integration. [0003] Existing photovoltaic power prediction methods include physical methods and statistical methods. Among them, the physical method is to establish the flu...

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Problems solved by technology

[0004] In order to overcome the defect of low prediction accuracy described in the above prior art,

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