Method and system for predicting photovoltaic power based on dynamic neural network

Abstract

The invention provides a method for predicting photovoltaic power based on a dynamic neural network. The method comprises the following steps of obtaining values which are respectively corresponding to weather characteristic parameters of a predicting daily in each time bucket in a set time bucket; dividing a weather type, identifying the weather type of the predicting daily based on the values obtained by the predicting daily through weighted Euclidean distance calculation, and constructing a similar day sample set of the predicting daily in history weather data according to the identified weather type; counting a number of days of the similar day sample set and solving a Chebyshev distance value thereof with the predicting daily for every day, and constructing a sample subset meeting a predetermined condition; carrying out normalization processing on the sample subset and training in a dynamic neural network prediction model; and after completing training, importing the values obtained by the predicting daily and carrying out renormalization processing to obtain photovoltaic power predicted values which are respectively corresponding the predicting daily in each time bucket of the set time bucket. By applying the embodiments of the invention, the predicting accuracy and the predicting speed can be simultaneously improved.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation in electric power systems, in particular to a method and system for predicting photovoltaic power based on a dynamic neural network. Background technique [0002] With the rapid development of the global economy, the increasing consumption of fossil energy and the continuous increase of carbon dioxide emissions, the adverse effects of environmental protection are becoming more and more serious. Therefore, energy and environmental issues have attracted great attention and active responses from all countries in the world. [0003] Under the strong demand for reducing fossil energy consumption and energy conservation and emission reduction, photovoltaic power generation, as a clean and renewable energy source, has a very broad application prospect. Due to the intermittent and random characteristics of photovoltaic power generation, its operation connected to the grid will bring ...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, according to the different mathematical and physical theories adopted and the types of predicted output, the methods for predicting photovoltaic power can be divided into statistical methods based on mathematical analysis and intelligent prediction algorithms, but both have shortcomings: statistics Although the prediction speed of the method is fast, it cannot accurately consider the influence of environmental factors on the photovoltaic output power; although the intelligent prediction algorithm has strong learning ability, is suitable for complex weather, and has relatively high prediction accuracy, the prediction model algorithm is complex and requires a large amount of computation.

Therefore, the traditional methods for predicting photovoltaic power have certain limitations and need to be improved and improved.

Images