The correlation of weather processes has a significant impact on the accuracy of photovoltaic power generation prediction.Therefore,a photovoltaic power prediction model based on dynamic recursive long short-term memory neural network was proposed.Firstly,model training was conducted by dynamically extracting meteorological factor features to capture the process meteorological changes of photovoltaic power plants under periodic and fluctuating characteristics,and modeling was carried out at same time;then,an effective information flow and state update mechanism composed of a bidirectional recurrent neural network and an improved long short-term recurrent neural network were used to correct the influence of procedural meteorological factors and output photovoltaic prediction power;finally,historical operating data was used for simulation verification.The experimental results show that the proposed method significantly reduces the average absolute error and root mean square error of the prediction results compared to traditional methods,confirming that the accuracy advantage of the proposed method can better meet the accuracy requirements of photovoltaic power prediction.
关键词
光伏功率预测/动态递归长短期神经网络/过程性气象变化/信息流动和状态更新机制/气象因素
Key words
photovoltaic power prediction/dynamic recurrent long short-term neural network/process-related weather variations/information flow and state updating mechanism/meteorological factors
维普
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