Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach

• Published in: Energy (Oxford) Vol. 238; p. 121764
• Main Authors: Emeksiz, Cem, Tan, Mustafa
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.2022; Elsevier BV
• Subjects: Artificial neural networks; Back propagation networks; Back propagation neural network; Decomposition; Empirical analysis; energy; Forecasting; Hurst analysis; Hybrid decomposition technique; Local mean decomposition; Mathematical models; Neural networks; Noise propagation; prediction; Prediction models; time series analysis; Wind farms; Wind power; Wind speed; Wind speed forecasting; Hybrid decomposition technique; Local mean decomposition; Hurst analysis; Back propagation neural network; Wind speed forecasting
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.121764

Wind speed should be predicted in a sensitive and reliable manner for the effective use of wind energy in wind farms. However, the volatility and non-linearity features of wind make it difficult to do so. Hence, many researchers have focused on the development of reliable prediction models for wind speed. Aimed at this challenge, the present study proposes a hybrid model comprised of Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN), Local Mean Decomposition (LMD), Hurst and Back-propagation Neural Network (BPNN). This model is actualized as follows: First, wind speed time series is decomposed into its sub-components via CEEMDAN technique. The least irregular and unsystematic of the IMFs with the highest frequency obtained as a result of decomposition via CEEMDAN is subject to secondary decomposition using the LMD technique. The obtained components are subject to Hurst analysis to be transformed into micro, meso and macro scale series. These series are then applied on feedback artificial neural networks. The analysis results indicate that model proposed has a better performance than the compared traditional forecasting methods (EEMD-VDM-BPNN and EEMD-EWT-BPNN) with regard to prediction accuracy. The MAPE values obtained via the proposed hybrid model were observed to have decreased by 41.16% and 78.80% when compared with those obtained using traditional models. •A novel hybrid model was improved for prediction of wind speed.•Forecasting accuracy and stability were improved via the suggested novel model.•The predictions confirmed the random behavior characteristic of wind.•The suggested hybrid model is very important tool for data mining applications.