SHORT-TERM WIND SPEED FORECASTING USING DEEP VARIATIONAL LSTM

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Volume 5 (2), December 2022, Pages 254-272

Navid Atashfaraz1 and Mohammad Manthouri2

1North Tehran Branch Azad University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

2Shahed University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Wind speed and power at wind power stations affect the efficiency of a wind farm, so accurate wind forecasting, a nonlinear signal with high fluctuations, increases security and better efficiency than wind power. We are looking for wind speed for a wind farm in Iran. In this research, a combined neural network created from variational autoencoder (VAE), long-term, short-term memory (LSTM), and multilayer perceptron (MLP) for dimension Reduction and encoding is proposed for predicting short-term wind speeds. The data used in this research is related to the statistics of 10 minutes of wind speed in 10- meter, 30-meter, and 40-meter wind turbines, the standard deviation of wind speed, air temperature, and humidity. To compare the proposed model (V- LSTM-MLP), we implemented three deep neural network models, including Stacked Auto-Encoder (SAE), recurrent neural networks (Regular LSTM), and hybrid model Encoder-Decoder recurrent network (LSTM-Encoder-MLP) presented on this dataset. According to the RMSE statistical index, the proposed model is worth 0.1127 for a short time and performs better than other types on this dataset.

Keywords:

LSTM, VAE, MLP, Wind Speed Prediction, Dimension Reduction, Encoder-Decoder.

DOI: https://doi.org/10.32010/26166127.2022.5.2.254.272

 

 

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