A CNN-BiLSTM based deep learning model for mid-term solar radiation prediction,International Transactions on Electrical Energy Systems

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A CNN-BiLSTM based deep learning model for mid-term solar radiation prediction
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2020-10-17 , DOI: 10.1002/2050-7038.12664
Amit Rai ₁ , Ashish Shrivastava ₂ , Kartick C Jana ₁

Affiliation

The penetrations of solar power plants are increasing their presence worldwide. The solar power plants have uncertain power output as its output depends on solar radiation, which is environmental dependent, so solar radiation prediction is a crucial step in integrating these plants into the power grid. In this work, a convolution neural network (CNN) and bi-direction long short term memory (BiLSTM) based hybrid deep learning (DL) model is proposed for effective midterm solar radiation prediction. The CNN architecture in this model captures the feature in solar radiation input data, and BiLSTM exploits the dependencies of this time series data. The proposed model is tested for three different geographical locations on the same latitude as it receives approximately the same solar radiation. The proposed hybrid DL model is compared with different recently proposed DL models. Moreover, distribution errors such as, skew and kurtosis errors are included for evaluating the distribution of predicted solar radiation. The outcome shows that the proposed hybrid DL model is robust and further enhancing the recently proposed DL models for midterm solar radiation prediction.

中文翻译：

基于CNN-BiLSTM的中期太阳辐射预测深度学习模型

太阳能发电厂的普及正在增加其在全球的影响力。太阳能发电厂的功率输出具有不确定性，因为其输出取决于太阳辐射，而太阳辐射又与环境有关，因此太阳辐射预测是将这些发电厂并网的关键步骤。在这项工作中，提出了一种基于卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 的混合深度学习 (DL) 模型，用于有效的中期太阳辐射预测。该模型中的 CNN 架构捕获了太阳辐射输入数据中的特征，而 BiLSTM 则利用了该时间序列数据的依赖关系。建议的模型在同一纬度上的三个不同地理位置进行了测试，因为它接收到大致相同的太阳辐射。将提出的混合 DL 模型与最近提出的不同 DL 模型进行比较。此外，还包括分布误差，例如偏斜和峰态误差，用于评估预测太阳辐射的分布。结果表明，所提出的混合 DL 模型是稳健的，并且进一步增强了最近提出的用于中期太阳辐射预测的 DL 模型。

更新日期：2020-10-17

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