The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.

中文翻译：

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基于LSTM的短期光伏发电量预测方法

光伏发电的间歇性和波动性严重影响输出功率的可靠性，效率，光伏电网的故障检测等。光伏发电的精确预测是解决上述局限性的关键方法。当前的光伏发电预测方法通常通常将气象数据和历史连续光伏发电作为输入，但是它们没有考虑历史周期性光伏发电作为输入，这使得现有方法在学习时间相关性方面不足。因此，为了进一步研究时间相关性以提高预测精度，提出了一种由长期短期记忆（LSTM）和完全连接（FC）层组成的LSTM-FC深度学习算法。该模型的双分支输入使其不仅可以考虑气象数据对发电的影响，还可以考虑时间连续性和周期依赖性，从而在一定程度上提高了预测精度。本文将气象数据，历史连续数据和历史定期数据用作实验数据，并将这三种类型的数据组合为不同的输入形式，以评估LSTM-FC并将其与其他基线模型进行比较，包括支持向量机（ SVM），梯度提升决策树（GBDT），广义回归神经网络（GRNN），前馈神经网络（FFNN）和LSTM。仿真结果表明，以气象数据，连续数据和周期数据为输入的模型的精度高于其他输入形式，