Short-term prediction of wind power based on deep Long Short-Term Memory

2016 ◽  
Author(s):  
Qu Xiaoyun ◽  
Kang Xiaoning ◽  
Zhang Chao ◽  
Jiang Shuai ◽  
Ma Xiuda
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Term Prediction ◽  
Long Short Term Memory ◽  
Short Term Prediction

scholarly journals Short term prediction of wind speed based on long-short term memory networks

2021 ◽  
Vol 49 (3) ◽  
pp. 643-652
Author(s):  
Umar Salman ◽  
Shafiqur Rehman ◽  
Basit Alawode ◽  
Luai Alhems
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Term Prediction ◽  
Cost Of Energy ◽  
Long Short Term Memory ◽  
Power Utilities ◽  
Short Term Prediction

Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.

Wind power forecast based on improved Long Short Term Memory network

Energy ◽  
2019 ◽  
Vol 189 ◽  
pp. 116300 ◽  
Author(s):  
Li Han ◽  
Huitian Jing ◽  
Rongchang Zhang ◽  
Zhiyu Gao
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Forecast ◽  
Memory Network ◽  
Long Short Term Memory

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

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3901 ◽  
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Observation Data ◽  
Short Term ◽  
Term Memory ◽  
Current Observation ◽  
Long Short Term Memory

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.

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