Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. (15th April 2021)

• Record Type: Journal Article
• Authors: Jaseena, K.U.; Kovoor, Binsu C.
• Is Part Of: Energy conversion and management. Volume 234(2021)
• Publication Date: 2021-04-15
• Subjects: Wind speed Forecasting -- Deep Learning -- Data Decomposition Techniques -- Bidirectional Long Short Term Memory Networks -- Empirical Wavelet Transform; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.113944
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Decomposition-based hybrid wind speed forecasting model using bidirectional LSTM. Decomposition techniques employed to denoise wind speed data are EMD, EEMD and EWT. Extensive evaluation in terms of accuracy and stability on two different datasets. Skip connections to enable training of deep networks for enhanced performance. Data denoising and skip connections significantly improve forecasting accuracy. Abstract: The goal of sustainable development can be attained by the efficient management of renewable energy resources. Wind energy is attracting attention worldwide due to its renewable and sustainable nature. Accurate wind speed prediction is essential for the stable functioning of wind turbines to generate wind power. However, the flexible and intermittent nature of wind speed makes accurate wind speed forecasting a challenging task. The proposed wind speed forecasting framework combines the features of various data decomposition techniques and Bidirectional Long Short Term Memory (BiDLSTM) networks. Presently, Data Decomposition models such as the Wavelet Transform are extensively employed for wind speed forecasting to improve the accuracy of the forecasting models. Hence, in this paper, various data decomposition techniques that can denoise the signal are investigated and applied to partition the input time series into several high and low-frequency signals. The data decomposition methods, namely, Wavelet Transform, Empirical Model Decomposition, Ensemble Empirical Mode Decomposition, and Empirical Wavelet Transform, have been applied to denoise the dataset. The low and high-frequency sub-series are forecasted separately using Bidirectional LSTM networks, and the forecasting outcomes of low and high-frequency signals are aggregated to get the final forecasting results. The empirical results establish that the proposed EWT- based hybrid model outperforms other decomposition-based models in accuracy and stability. The performance of the EWT-BiDLSTM model is further compared with Bidirectional LSTM networks with skip connections. The experimental results substantiate that the proposed decomposition-based hybrid deep BiDLSTM models with skip connections exhibit better prediction accuracy than other models.