Short-term wind speed forecasting based on long short-term memory and improved BP neural network. (January 2022)

• Record Type: Journal Article
• Authors: Chen, Gonggui; Tang, Bangrui; Zeng, Xianjun; Zhou, Ping; Kang, Peng; Long, Hongyu
• Is Part Of: International journal of electrical power & energy systems. Volume 134(2022)
• Publication Date: 2022-01
• Subjects: Short-term Wind Speed Forecasting -- Data Preprocessing -- Fuzzy Entropy (FE) -- LSTM -- Improved Sparrow Search Algorithm-BP (ISSA-BP); Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107365
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Accurate and reasonable wind speed prediction is very necessary. Singular spectrum analysis has a good de-noising effect on time series signals. An excellent data decomposition algorithm, called CEEMDAN, is adopted. A uniform mutation operator is proposed to improve the sparrow search algorithm. The optimized BP neural network and LSTM are used to predict wind speed. Abstract: Accurate and reasonable wind speed prediction system has a significant impact on the utilization of wind energy. A novel combination forecasting model based on Long Short-Term Memory (LSTM) network and BP neural network is designed in this paper. This model combines the principle of deep learning algorithm and the improved BP neural network to deal with nonlinear wind speed prediction. Before the prediction, singular spectrum analysis (ssa) and complete ensemble empirical model decomposition adaptive noise (CEEMDAN) are selected as the data pretreatment part to de-noise the original wind speed data and decompose it into multiple components. This part is conducive to improving the signal-to-noise ratio (SNR) of wind speed data and simplifying the characteristics of wind speed data. Then, in order to reduce the error accumulation and computation redundancy, fuzzy entropy (FE) is used to calculate the time complexity of each component, according to the Spearman correlation, the inherent mode function (IMF) components are recombined to form a new subsequence. Experimental results show that the error accumulation can be reduced by 48.65% for dataset 1 and 29.53% for dataset 2, and the operation time can be reduced by about 50% for two datasets. To avoid the limitation of a single model, introducing the LSTM and improved BPNN which improved by sparrow search algorithm (SSA) two different prediction models are used to predict the sub sequences with high complexity and the low complexity subsequences, respectively. Finally, the predicted values of the models are superimposed to get the final values. In order to verify the validity of the proposed model, the final predictions, compared with six different prediction models, show that this model can achieve the best performance and obtain higher prediction accuracy. Such as the performance evaluation indexes (RMSE = 0.051, MAPE = 0.929%) are smallest obtained from dataset1 by one-step prediction, and (RMSE = 0.086, MAPE = 0.966%) are smallest obtained from dataset2 by one-step prediction. In addition, the Pearson correlation between the predicted value and the true wind speed value obtained by the prediction model applied to the two data sets is the highest 99.17% and 98.73%, respectively.