Photovoltaic power station power prediction method and system based on recurrent neural network

Abstract

The invention relates to a photovoltaic power station power prediction method and system based on a recurrent neural network. The method comprises steps of acquiring historical output power data and weather forecast data recorded by a photovoltaic power station; performing data processing to obtain a historical output power data time sequence and a corresponding historical meteorological data time sequence, and performing normalization processing and segmentation to form a sample data set; and constructing and training a recurrent neural network model. Collecting output power data in a period of time, performing data processing, and inputting the data into the recurrent neural network model; and outputting a prediction result by the recurrent neural network model, and obtaining corresponding output power data as a photovoltaic power station power prediction value. According to the method, the photovoltaic power prediction model based on the recurrent neural network model is trained by combining the historical data of the photovoltaic power station and the NWP weather forecast data, the photovoltaic power generation power in the next 24 hours is predicted, and the prediction precision is 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 the power of a photovoltaic power station based on a cyclic neural network. Background technique [0002] In recent years, with the rapid economic growth, more and more environmental problems have emerged, and the carbon dioxide greenhouse gas has soared, posing a threat to the living system. In this context, countries around the world have adopted a global agreement to reduce greenhouse gas emissions. In order to achieve this great goal, all regions should actively adjust the energy structure, optimize the industrial layout, develop new energy industries, and adhere to green and low-carbon development. As the focus of carbon emissions, the power industry must vigorously develop photovoltaic and other new energy power generation industries and continuously optimize the power structure. The so-called photovoltaic po...

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Problems solved by technology

However, the extrapolation ability of this kind of method is limited. Usually, it can only have a high accuracy within 1 to 4 hours, and the accuracy rapidly decays after more than 4 hours. Therefore, the accuracy and effective duration of photovoltaic power prediction need to be improved.

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