Dynamic weight fusion of three integrated learning models of XGBoost

Light GBM and NGBoost is carried out to retain feature correlation and correction coefficient is introduced to reduce the effect of accumulation of errors on prediction accuracy.On this basis

the final fusion model is used to predict the photovoltaic power generation of the photovoltaic array at the location of Connellan Airport in central Australia on a monthly frequency.At the same time

three kinds of machine learning regression models

multilayer perceptron

support vector regression and long short-term memory network are introduced for comparative experiments.The prediction result shows that the performance of photovoltaic（ PV） power generation dynamic correction prediction based on integrated learning model fusion is significantly prior to other prediction models which introduced as comparison models

and the mean absolute error is improved by 10 % ～ 19 %

which is also an advantage over the en-semble learning model that only uses weight fusion

with an increase of 14 %.