201. Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group-deep gate recurrent neural network.
- Author
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Lei Zhang, Shuang Zhao, Guanchao Zhao, Lingyi Wang, Baolin Liu, Zhimin Na, Zhijian Liu, Zhongming Yu, Wei He, Mrzljak, Vedran, and Ling-Ling Li
- Subjects
RECURRENT neural networks ,SOLAR technology ,ARTIFICIAL neural networks ,ELECTRIC power engineering ,GENERATIVE adversarial networks ,PHOTOVOLTAIC power generation ,PHOTOVOLTAIC power systems - Abstract
In response to the issue of short-term fluctuations in photovoltaic (PV) output due to Cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep Gate Recurrent Neural Network (DGN). Initially, a cloud motion prediction model is constructed using a DSCVGG, which achieves edge recognition and motion prediction of clouds by replacing the previous convolution layer of the pooling layer in VGG with a depthwise separable convolution. Subsequently, the output results of the DSCVGG network, along with historical PV output data, are introduced into a Deep Gate Recurrent Unit Network (DGN) to establish a PV output prediction model, thereby achieving precise prediction of PV output. Through experiments on actual data, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of our model are only 2.18% and 5.32 x 10
-5 , respectively, which validates the effectiveness, accuracy, and superiority of the proposed method. This provides new insights and methods for improving the stability of PV power generation. [ABSTRACT FROM AUTHOR]- Published
- 2024
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