Abstract
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.
Highlights
As a green renewable energy source, wind power has an immeasurable commercial development prospect, and the research on related forecasting technologies is more important
The Hilbert–Huang transform (HHT) [33], fast correlation filter [34], principal component analysis (PCA) [35], and so on extracted the input features of wind speed data and obtained good prediction results by optimizing the short-term wind speed prediction model combined with other prediction methods
Is paper is organized as follows: in Section 2, we constructed a wind speed prediction model based on long short-term memory (LSTM); in Section 3, we studied the firework algorithm, hyperparameters optimization of LSTM by the firework algorithm, and optimized LSTM wind speed prediction algorithm based on firework algorithm
Summary
As a green renewable energy source, wind power has an immeasurable commercial development prospect, and the research on related forecasting technologies is more important. Statistical methods require a large amount of data for learning and modeling and are more suitable for ultrashort-term wind power prediction. Intelligent learning methods, such as Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), train and predict the wind speed data with better performance in the fitting of the nonlinear changes of wind speed [5,6,7]. The Hilbert–Huang transform (HHT) [33], fast correlation filter [34], principal component analysis (PCA) [35], and so on extracted the input features of wind speed data and obtained good prediction results by optimizing the short-term wind speed prediction model combined with other prediction methods.
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