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Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting

机译：基于门控循环单元网络的短期光伏预测

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Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.
机译：由于环境因素，光伏电源具有很大的波动性和间歇性。预测光伏发电对确保配电网安全经济运行具有重要意义。本文提出了一种基于门控循环单元（GRU）网络的短期光伏发电预测方法。首先，利用皮尔逊系数提取影响下一时刻光伏发电量的主要特征，并定性分析历史光伏发电量与未来光伏发电量之间的关系。其次，利用K-means方法根据每个特征的相似性将训练集分为几组，然后将GRU网络训练应用于每组。平均每个GRU网络的输出，以在下一刻获得光伏发电输出。实例研究表明，该方法可以有效地考虑特征和历史光伏发电对未来光伏发电的影响，比传统方法具有更高的精度。

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