![]() ![]() Commonly used statistical methods are time series modeling ( Liu et al., 2020b), Kalman filtering ( Paliwal and Basu, 1987), Markov chain ( Sahin and Sen, 2001), Bayesian method ( Liu et al., 2020a) and so on. Statistical models learn the patterns of historical wind speed data and establish non-linear mapping relationships between the data, thus realizing time series forecasting ( Rodrigues Moreno et al., 2020). In contrast, statistical models are more suitable for short-term wind speed forecasting. These methods are usually time-consuming and unsuitable for short-term and ultrashort-term wind speed forecasting due to excessive model considerations and model over-complexity ( Wang and Li, 2018). Physical process-driven models are mostly numerical weather prediction (NWP) models ( Lowery and O’Malley, 2012), which make predictions based on local environmental information, such as, temperature, humidity, and geography. Data-driven models are divided into statistical models ( Liu et al., 2010) and artificial intelligence models ( Khodayar et al., 2017). Therefore, proposing a method to accurately predict the short-term wind speed has an important impact on the economic and reliable operation of the power system ( Rizwan-ul-Hassan et al., 2021).Ĭurrently, short-term wind speed prediction methods are divided into two main categories: physical process-driven models ( Higashiyama et al., 2018) and data-driven models ( Yuan et al., 2017). Among them, short-term wind speed prediction is an indispensable factor for the development of daily scheduling plans. However, the stochastic, fluctuating and intermittent nature of wind farms poses significant challenges to the operation and control of the entire power system including wind farms ( Lacal-Arantegui, 2019). According to the latest report released by the Global Wind Energy Council (GWEC) ( Guliyev, 2020), the global installed capacity of wind power will reach 743 GW in 2020, with a 53% year-on-year growth in new installations. Wind energy plays an important role in many new energy sources. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains. ![]() The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.ĭiscussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. 2School of Sciences, Guangxi University of Science and Technology, Liuzhou, China.1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
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