基于分时长短期记忆神经网络的光伏发电超短期功率预测
摘要：准确预测光伏发电功率对电力系统运行调度至关重要。提出一种基于Spearman相关系数和分时长短期记忆网络的光伏发电功率预测方法。首先利用Spearman相关系数分析每个时刻下影响光伏发电功率的因素，选取相关度高的影响因素作为长短期记忆网络模型的输入变量；然后，对每个时刻建立一个基于长短期记忆网络的预测模型，实现分时光伏发电功率的预测。最后，利用实际光伏发电站的历史发电功率和数值天气预报数据进行案例分析。结果表明，所提方法比单一长短期记忆网络预测模型具有更高的预测精度。
Abstract:It is significant for power system operation and dispatching to forecast photovoltaic (PV) power generation output accurately. Based on Spearman correlation coefficient analysis and time-division long short-term memory network, this paper proposed a new ultra short-term forecasting method for PV generation output. First, the Spearman correlation coefficient was utilized to analyze the factors influencing the PV power generation at every moment, and the influencing factors with high correlation degrees were selected as the input variables of the long short-term memory network. Secondly, for each moment a forecasting model based on the long short-term memory network was established to carry the forecasting of the output of time-division PV power generation. Finally, utilizing the data from historical generated power of actual PV power station and the data from numerical weather prediction (NWP) the case study was performed. Results of calculating examples show that using the proposed method, a more accurate forecasting can be obtained than the results obtained by only using the forecasting model based on the time-division long short-term memory network.
标题：基于分时长短期记忆神经网络的光伏发电超短期功率预测
英文标题：Ultra Short-term Forecast of Photovoltaic Generation Based on Time-division Long Short-term Memory Neural Networks
作者：田剑刚, 张沛, 彭春华, 时珉, 王铁强, 尹瑞, 王一峰,
关键词：光伏发电, 超短期功率预测, 长短期记忆网络, Spearman相关系数, 分时预测,
发表日期：2020-12-10
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