In recent years

a spurt of photovoltaic power generation has brought certain impact on stability of the power system

which puts forward higher requirements on accuracy of photovoltaic power prediction. Therefore

this paper proposes a hybrid power prediction model based on fluctuation classification and feature factor extraction. First

based on fluctuation characteristics of photovoltaic power

fluctuation classification is applied to forecast power before the day

and weather is divided into complex fluctuation types and simple types. Then

parallel factor algorithm is used to reduce prediction model redundancy

which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. Finally

the Long Short-Term Memory (LSTM) deep learning model is used to forecast very short-term photovoltaic power. The proposed hybrid model is compared with other methods

and photovoltaic data from several sites are selected for comparison and validation in this paper. Simulation results show that very short-term prediction method of photovoltaic power proposed in this paper can significantly improve prediction accuracy.