Abstract
With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms.
Highlights
IntroductionAlong with the rapid development of technology in the last few decades, energy demands continue to increase rapidly [1]
Energy plays a vital part in modern social and economic development
Singular Spectrum Analysis (SSA), as a novel analytical method, is especially suitable for research into periodic oscillation, which has proven to be an effective tool for time series analysis in diverse applications; the results indicate that it can effectively remove the noise of the wind speed data to improve forecasting performance
Summary
Along with the rapid development of technology in the last few decades, energy demands continue to increase rapidly [1]. In accordance with the IEA World Energy Outlook 2010, China and India will be responsible for approximately 50% of the growth in global energy demand by 2050. Second only to America, China will become the second leading energy-consuming country in the world. Using traditional resources produces large amounts of carbon dioxide, which may lead to global warming, and is considered an international security threat. It affects the environment, it threatens the safety of individuals and the planet [3]
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