Abstract
With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.
Highlights
IntroductionIn the past few decades, to reduce dependence on fossil fuels with their negative effects on the environment, attention has turned to clean renewable energy sources throughout the world [1]
In the past few decades, to reduce dependence on fossil fuels with their negative effects on the environment, attention has turned to clean renewable energy sources throughout the world [1].As one kind of the rapidly growing renewable energy sources, wind energy has been recognized as an attractive alternative to conventional fossil fuels due to several advantages, including renewability and pollution-free environment [2]
The trend component can be decomposed into a number of intrinsic mode functions (IMFs) and a residual with different frequencies
Summary
In the past few decades, to reduce dependence on fossil fuels with their negative effects on the environment, attention has turned to clean renewable energy sources throughout the world [1]. As one kind of the rapidly growing renewable energy sources, wind energy has been recognized as an attractive alternative to conventional fossil fuels due to several advantages, including renewability and pollution-free environment [2]. Wind power is recognized as a stochastic process [3] because of the intermittent and multi-scale characteristics of wind speed fluctuation [4,5]. Improving the prediction accuracy of wind speed is beneficial for increasing the security of wind energy utilization and reducing the risk of power outages [7]
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