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Article

Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

1
Department of Information and Communication Engineering, Honam University, Gwangsan-gu 62399, Korea
2
Department of Electrical Engineering, Honam University, Gwangsan-gu 62399, Korea
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(20), 3901; https://doi.org/10.3390/en12203901
Received: 8 July 2019 / Revised: 16 September 2019 / Accepted: 12 October 2019 / Published: 15 October 2019
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models. View Full-Text
Keywords: wind power prediction; multivariate models; hybrid model; long short-term memory wind power prediction; multivariate models; hybrid model; long short-term memory
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MDPI and ACS Style

Son, N.; Yang, S.; Na, J. Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory. Energies 2019, 12, 3901. https://doi.org/10.3390/en12203901

AMA Style

Son N, Yang S, Na J. Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory. Energies. 2019; 12(20):3901. https://doi.org/10.3390/en12203901

Chicago/Turabian Style

Son, Namrye, Seunghak Yang, and Jeongseung Na. 2019. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory" Energies 12, no. 20: 3901. https://doi.org/10.3390/en12203901

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