Method for predicting solar power generation amount based on adaptive learning hybrid model

Abstract

The invention discloses a method for predicting the solar power generation amount based on an adaptive learning hybrid model. The method includes the steps of firstly, obtaining meteorological variable data from real-time meteorological information; secondly, subjecting the data to a hybrid model formed by combining a time-varying multivariate model and a genetic algorithm optimized BP neural network model to obtain an initial predicted value and a final predicted value of the solar power generation amount, and calculating a corresponding prediction error value and prediction mean square errorvalue; and then, improving the prediction accuracy through an adaptive learning method. The method of the invention is mainly applied to the energy prediction of a microgrid, effectively improves theprediction accuracy of the solar power generation amount and can make the energy management of the microgrid more efficient.

Description

technical field [0001] The invention relates to a method for predicting solar power generation, in particular to a method for predicting solar power generation based on an adaptive learning hybrid model in a microgrid. Background technique [0002] Renewable energy is inexhaustible and inexhaustible energy. For the sustainable development of human society, countries all over the world have turned their attention to renewable energy, and solar power is the main use of renewable energy. It is a smart The main components of the grid. A key goal of smart grid efforts is to greatly increase the utilization of environmentally friendly renewable energy, and microgrid technology is a key technology to achieve this goal, but the uncontrollability of renewable energy generation has brought challenges to our microgrid energy management. Difficulties have caused serious impacts and threats to the economical, safe, and stable operation of the microgrid. Therefore, it is very important t...

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Problems solved by technology

Existing prediction methods for solar power generation are mainly statistical methods and artificial neural network methods. Statistical methods use probability theory to find out its internal laws and use them for prediction through statistical analysis of historical data; The neural network method uses sample data as input to establish a prediction model to predict future power generation; the above two methods can achieve high prediction accuracy for data information with strong regularity and periodicity, but solar energy has randomness , volatility and other characteristics, using these two methods, the prediction effect is very unsatisfactory, unable to meet the needs of existing energy management, which greatly limits the efficiency and reliability of microgrid energy management

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