An accurate power output prediction of the photovoltaic system is pivotal to eliminate the extra cost and the negative impact in the utility grid integrated with photovoltaic power sources. The power output of a photovoltaic system is predicted by introducing a long short-term memory method. Moreover, the influence of noise data on prediction results is eliminated with the empirical mode decomposition. To further improve the accuracy and stability of the prediction method, the parameters of long short-term memory neural networks are determined with a sine cosine algorithm. The performances of the long short-term memory method in terms of root mean square error, mean absolute error, and coefficient of determination in January and August are analysed, respectively. Compared with other prediction schemes, the long short-term memory method provides superior accuracy for photovoltaic power output prediction.

中文翻译：

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基于长短期记忆网络的光伏功率输出准确预测

光伏系统准确的功率输出预测至关重要，以消除与光伏电源集成的公用电网中的额外成本和负面影响。通过引入长期的短期记忆方法可以预测光伏系统的功率输出。此外，通过经验模态分解消除了噪声数据对预测结果的影响。为了进一步提高预测方法的准确性和稳定性，使用正弦余弦算法确定长短期记忆神经网络的参数。从一月和八月的均方根误差，平均绝对误差和确定系数方面分析了长期短期记忆方法的性能。与其他预测方案相比，