Abstract
Accurate wind speed forecasting is essential for the reliability and security of the power system, and optimal operation and management of wind integrated smart grids. However, it is still a challenging task due to the highly uncertain and volatile nature of wind speed. Accordingly, in this work, a novel deep learning-based model integrating the discrete wavelet packet transform (DWPT) and bidirectional long short-term memory (BLSTM) is developed to precisely capture deep temporal features and learn the time-varying relationship of wind speed time series. In the proposed method, by applying the DWPT, both approximations and details parts are decomposed by passing through the filters to choose the frequency band related to the features of the original signal more adaptively. The BLSTM networks are incorporated to deal with the uncertainties more effectively as they have bidirectional memory capability (feedforward and feedback loops) to investigate both previous and future hidden layers data. To simultaneously improve the forecasting performance and decrease the learning complexity, the reconstructed state space of historical wind data is employed to reflect the evolution laws of wind speed. Two case studies using real-world wind speed datasets gathered from Flatirons campus (M2) of National Renewable Energy Laboratory (NREL) located in Colorado, USA and weather station of Edmonton, Canada are implemented to demonstrate the effectiveness and superiority of the proposed hybrid method compared to the shallow architectures and state-of-the-art deep learning models in the recent literature.
Highlights
I N recent decades, the research and development of renewable energies have gradually increased around the world as an appealing solution to the high greenhouse gases’ emissions of fossil fuel-based energy resources, which raised worldwide concerns [1]
National Renewable Energy Laboratory (NREL) M2 WIND SPEED DATASET After tuning the parameters and specifying the optimal structure, the model is trained by using the training set
The average time required to train the network is entirely dependent on the structure complexity, and for the proposed model, it is about 10 minutes, which makes it applicable for realtime wind speed forecasting purposes
Summary
I N recent decades, the research and development of renewable energies have gradually increased around the world as an appealing solution to the high greenhouse gases’ emissions of fossil fuel-based energy resources, which raised worldwide concerns [1]. The total installed capacity of wind power in Canada has increased from 2,349 MW in 2008 to 12,816 MW in 2018 by an annual rate of 20% in the past ten years [2]. The wind power generation mainly depends on the wind speed, which can dramatically fluctuate in few seconds and directly affect the stability, resilience, and robustness of the power system. For this reason, accurate wind speed prediction facilitates wind power facilities integration into modern power systems
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