Wind Speed Prediction Model Using LSTM and 1D-CNN

Abstract

This paper describes a prediction method for wind speed using a neural network and an investigation of the structure of the network. Generally, wind speed is observed as time series data, and the current wind speed is related to the past wind speed. Therefore, we propose a prediction model using long short-term memory (LSTM) and a one-dimensional convolutional neural network (1DCNN) in order to consider the past information for prediction. The prediction results of these networks and a fully connected neural network are compared for evaluation. The prediction accuracy and time delay are found to be improved by using LSTM and the 1D-CNN.