Photovoltaic power generation power short-term prediction method and device

Abstract

The invention discloses a photovoltaic power generation power short-term prediction method and device. The photovoltaic power generation power short-term prediction device comprises a data input, output and processing module, a model parameter optimization module and a prediction model training and application module, wherein the data input/output and processing module comprises an original data normalization and prediction result reverse normalization unit, a GA coding unit and a GA fitness function calculation unit; the model parameter optimization module comprises an RBM initial parameter optimization unit and an RBM model training and NARX parameter initialization unit; the prediction model training and application module is an NARX neural network training and application unit.

Description

technical field [0001] The invention belongs to the field of photovoltaic power generation, and in particular relates to a method and device for short-term prediction of distributed photovoltaic power generation power based on an improved NARX neural network algorithm. Background technique [0002] The photovoltaic power generation system is obviously affected by the environment, which is manifested in volatility, uncertainty and intermittency. Since the change of the environment will directly affect the power of photovoltaic power generation, the prediction accuracy of photovoltaic power generation will be reduced. Therefore, it is of great significance to establish an effective short-term prediction model of photovoltaic power generation for the security dispatching and economic management of the power grid. [0003] So far, scholars have conducted different levels of research on photovoltaic power forecasting, and the research methods mainly include two types: statistical...

AI Technical Summary

Problems solved by technology

Compared with the traditional BP neural network, the NARX neural network increases the learning ability of time series, but it inherits the characteristics of the generalized neural network to a certain extent, and has the following defects: slow convergence speed, easy to fall into local optimum

At the same time, there are also literatures that propose a short-ter

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