针对太阳辐射引起光伏出力的不确定性和波动性,进而造成大量光伏发电并网时对电网稳定性和安全的危害,提出一种新的太阳辐射超短期预测方法。该方法通过构建一维卷积神经网络,对多个关键气象变量进行数据融合和特征转换,然后构造双向长短期记忆网络预测模型,实现对未来15 min的太阳总辐照度的超短期预测。实验结果表明,所提出的预测模型相对传统的机器学习方法可有效提高超短期太阳总辐照度的预测精度,且相对持续模型在相对方差上提高了约14%。
Abstract
Since solar radiation causes the uncertainties and fluctuation of photovoltaic power, which is harmful to the stability and safety of the grid when photovoltaic power generation is connected to the grid, a new super-short-term prediction model of solar radiation whas proposed in this paper. Firstly, a one-dimensional convolution neural network is constructed for data fusion and feature transformation of several key meteorological variables; and then a bidirectional Long Short Term Memory network model is developed to predict global solar radiation in the next 15 minutes. The experimental results show that the proposed model can effectively improve the predicting accuracy compared with the traditional machine learning methods, and that the forecast skill of the proposed model has been improved about 14% over the persistence model on the normalized root mean squared errors.
关键词
太阳辐照度 /
预测 /
卷积神经网络 /
超短期 /
双向长短期记忆网络
Key words
solar irradiance /
forecasting /
convolutional neural networks /
super-short-term /
bidirectional long short-term memory
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基金
国家自然科学基金青年项目（62006120）; 东南大学复杂工程系统测量与控制教育部重点实验室开放课题（MCCSE2020A02）
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