Statistical downscaling of numerical weather prediction based on convolutional neural networks

Hongwei Yang, Jie Yan, Yongqian Liu, Zongpeng Song

Global Energy Interconnection · 01 April 2022

In one sentence. CNN-based statistical downscaling of NWP fields to turbine hub height matches physical-model accuracy for wind power forecasting at one farm.

Abstract

Numerical Weather Prediction (NWP) is a necessary input for short-term wind power forecasting. Existing NWP models are all based on purely physical models. This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high. There is a need to further improve the timeliness and accuracy of the assimilation process. In order to solve the above problems, NWP method based on artificial intelligence is proposed in this paper. It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position. We considered the actual data of a wind farm in north China as an example to analyze the calculation example. The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model. The prediction accuracy in some months is better than that of the purely physical model, and the calculation efficiency is considerably improved. The validity and advantages of the proposed method are verified from the results, and the traditional NWP method is replaced to a certain extent.

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