A Hour-Ahead Wind Speed Forecasting Using One-Dimensional Convolutional Neural Network

With increasing penetration of wind power, accurately predicting wind speeds are essential for planning and operation of power grids. In this paper, a short-term deep learning-based wind speed forecasting approach is proposed using one-dimensional convolutional neural network (1D CNN), in which 1D CNN aggregates the weather information of the last hour to predict a hour-ahead wind speed accurately. The input feature selection, data preprocessing, and model evaluation are discussed in this paper. Wind speed at a specific time can be predicted in less than a few milliseconds using the proposed approach along with the meteorological data measured an hour earlier. Three years historical wind speed data from 2020 to 2022 measured in Saskatoon International Airport, Saskatoon, Saskatchewan, Canada are used in this study. Experimental results verify that this 1D CNN-based wind speed forecasting technique provides accurate wind speed prediction. It can contribute to sustainable energy development in Saskatchewan and beyond.