Deep Learning and Neural Network-Based Wind Speed Prediction Model

Abstract

This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and Group Method of Data Handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).