This paper investigates a deep learning-based wind-forecasting model to establish an accurate forecasting model which can support the increasing growth of wind power generation. The wind forecasting means wind speed and direction forecasting at the same time. Proposed forecasting model consists of three-dimensional convolutional neural network and deep convolutional long short-term memory (3DCNN-DConvLSTM), and forecasts the wind vector which expressed as time-sequential images. DConvLSTM model learns spatiotemporal features from time-series image data that represent a spatial and temporal change of wind speed and direction. The forecasting model combined of 3DCNN and DConvLSTM is effective to decrease training time, and forecasting error in comparison to the DConvLSTM model. Input of the forecasting model is wind speed and direction that is expressed as an image on 2D coordinate and uses the measured data by the Automated Meteorological Data Acquisition System (AMeDAS), Japan. Forecasting accuracy with one-hour ahead and its usefulness of the proposed forecasting model is evaluated with simulation results for four seasons that is typical of Japan climate, and demonstrated by comparison with fully connected-LSTM (FC-LSTM), encoder-decoder based 3DCNN (ED-3DCNN), DConvLSTM, and persistent models. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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3DCNN 和深度卷积 LSTM 的短期风速和风向预测

本文研究了一种基于深度学习的风力预测模型，以建立一个准确的预测模型，以支持风力发电的不断增长。风速预报是指同时预报风速和风向。提出的预测模型由三维卷积神经网络和深度卷积长短期记忆（3DCNN-DConvLSTM）组成，并预测风向量，并以时序图像表示。DConvLSTM 模型从时间序列图像数据中学习时空特征，这些数据表示风速和风向的时空变化。与 DConvLSTM 模型相比，3DCNN 和 DConvLSTM 相结合的预测模型有效地减少了训练时间和预测误差。预测模型的输入是风速和风向，以二维坐标图像表示，并使用日本自动气象数据采集系统 (AMeDAS) 的测量数据。用日本典型气候的四个季节的模拟结果评估提前一小时的预测准确性及其所提出的预测模型的实用性，并通过与基于编码器-解码器的全连接 LSTM (FC-LSTM) 的 3DCNN 进行比较来证明(ED-3DCNN)、DConvLSTM 和持久模型。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。用日本典型气候的四个季节的模拟结果评估提前一小时的预测准确性及其所提出的预测模型的实用性，并通过与基于编码器-解码器的全连接 LSTM (FC-LSTM) 的 3DCNN 进行比较来证明(ED-3DCNN)、DConvLSTM 和持久模型。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。用日本典型气候的四个季节的模拟结果评估提前一小时的预测准确性及其所提出的预测模型的实用性，并通过与基于编码器-解码器的全连接 LSTM (FC-LSTM) 的 3DCNN 进行比较来证明(ED-3DCNN)、DConvLSTM 和持久模型。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。