scholarly journals Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bilin Shao ◽  
Dan Song ◽  
Genqing Bian ◽  
Yu Zhao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Energy ◽  
Prediction Model ◽  
Short Term Memory ◽  
Sudden Change ◽  
Regression Methods ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

Wind energy is a renewable energy source with great development potential, and a reliable and accurate prediction of wind speed is the basis for the effective utilization of wind energy. Aiming at hyperparameter optimization in a combined forecasting method, a wind speed prediction model based on the long short-term memory (LSTM) neural network optimized by the firework algorithm (FWA) is proposed. Focusing on the real-time sudden change and dependence of wind speed data, a wind speed prediction model based on LSTM is established, and FWA is used to optimize the hyperparameters of the model so that the model can set parameters adaptively. Then, the optimized model is compared with the wind speed prediction based on other deep neural architectures and regression models in experiments, and the results show that the wind speed model based on FWA-improved LSTM reduces the prediction error when compared with other wind speed prediction-based regression methods and obtains higher prediction accuracy than other deep neural architectures.

scholarly journals Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Athraa Ali Kadhem ◽  
Noor Wahab ◽  
Ishak Aris ◽  
Jasronita Jasni ◽  
Ahmed Abdalla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction ◽  
Artificial Neural

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

Wind Speed Prediction Based on Improved Grey Neural Network Model

2014 ◽  
Vol 548-549 ◽  
pp. 1235-1240
Author(s):  
Bin Zeng ◽  
Jian Xiao Zou ◽  
Kai Li ◽  
Xiao Shuai Xin
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Data Storage ◽  
Wind Farm ◽  
Grey System Theory ◽  
Grey System ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

scholarly journals Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Prediction Model ◽  
Short Term Memory ◽  
Memory Capacity ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Long Short Term Memory ◽  
Model Networks ◽  
With Memory

Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.

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