A Hybrid Wind Speed Prediction Model Based on Signal Decomposition and Deep 1DCNN

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.

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An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

Author(s):

Zhongda Tian ◽

Yi Ren ◽

Gang Wang

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Least Squares ◽

Support Vector ◽

Short Term ◽

Backtracking Search ◽

Wind Speed Prediction ◽

Search Optimization ◽

Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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