Short‐Term Wind Speed and Direction Forecasting by 3DCNN and Deep Convolutional LSTM

Abstract 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.