Short-term Power Prediction System for Photovoltaic Power Generation Based on LSTM Model
Photovoltaic power generation is affected by weather factors and has obvious intermittent and fluctuating characteristics.In this paper,a short-term prediction method of photovoltaic power generation based on LSTM network model is proposed,which uses STM32 microcontroller as the control core to collect data such as radiance,tempera-ture,relative humidity,and wind speed in real time.The correlation coefficient method is used to screen the factors with high correlation and use them as input variables of the LSTM network model to make short-term predictions of future photovoltaic power generation.The results of MATLAB simulation experiments show that the proposed method has high prediction accuracy compared with other prediction models,and the MAPE values predicted in sunny and cloudy weather are 4.943％and 4.997％respectively,which is conducive to the stable operation of China's power system and the dispatch of power grid staff.
STM32 MCUshort-term forecastingLSTM network modelreal-time collectionphotovoltaic power generation
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万方数据
