Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed

Meftah Elsaraiti,Adel Merabet

doi:10.3390/app11052387

Abstract

Forecasting wind speed is one of the most important and challenging problems in the wind power prediction for electricity generation. Long short-term memory was used as a solution to short-term memory to address the problem of the disappearance or explosion of gradient information during the training process experienced by the recurrent neural network (RNN) when used to study time series. In this study, this problem is addressed by proposing a prediction model based on long short-term memory and a deep neural network developed to forecast the wind speed values of multiple time steps in the future. The weather database in Halifax, Canada was used as a source for two series of wind speeds per hour. Two different seasons spring (March 2015) and summer (July 2015) were used for training and testing the forecasting model. The results showed that the use of the proposed model can effectively improve the accuracy of wind speed prediction.

Highlights

Forecasting wind speed is a very difficult challenge compared to other variables of the atmosphere, and this is due to their chaotic and intermittent nature which causes difficulty in integrating wind power to the grid
Analyze the accuracysoftware of the models, among most used pa- of. In this the wasthe used for commonly the training process rameters for estimating wind speed predictions is root mean square error (RMSE)
The Long short-term memory (LSTM), which is an advanced architecture for recurrent neural network (RNN) to predict the values of futureIttime expressed steps ofby a sequence

Summary

Introduction

Forecasting wind speed is a very difficult challenge compared to other variables of the atmosphere, and this is due to their chaotic and intermittent nature which causes difficulty in integrating wind power to the grid. Time series forecasting is one of the most important applied problems of machine learning and artificial intelligence in general, since the improvement of forecasting methods will make it possible to more accurately predict the behavior of various factors in different areas Such models are based on the methods of statistical analysis and mathematical modeling developed in the 1960s and 1970s [2]. The availability of large datasets combined with the improvement in algorithms and the exponential growth in computing power led to an unparalleled surge of interest in the topic of machine learning These methods use only historical data to learn the random dependencies between the past and the future. Recurrent neural networks (RNNs) which are designed to learn a sequence of data by traversing a hidden state from one step of the sequence to the combined with the input, and routing it back and forth between the inputs [5]

Methods

Results

Conclusion