A hybrid intelligent framework for forecasting short-term hourly wind speed based on machine learning

With the development of wind power which is the great substitute for traditional energy, it is worth conducting an in-depth exploration of the hourly wind speed time series which is chaotic due to the complex weather. In this paper, the hybrid intelligent framework is proposed as an integrated prediction tool with high accuracy for signal pre-processing, data prediction, and result optimization of short-term hourly wind speed. Specifically, its excellent performance is guaranteed through adequate information extraction on three stages. The first stage is completed in the signal pre-processing module that the interference information is cleaned up from the hourly wind speed signal via de-noising. The second stage is conducted in the data prediction module that the hidden regular information is fully extracted via the decomposition method. The third stage is performed in the resulting optimization module that the residual information is recovered via error modification. For illustration, the performance of the proposed framework is evaluated through historical hourly wind speed, taken from publicly available Sotavento wind farms. The obtained experimental results indicate that de-noising is beneficial to capturing the real trend, but it may negatively impact short-term prediction accuracy. For the hybrid prediction model based on the empirical mode decomposition-based method, the de-noising mode integrating into the decomposition process is more effective than independent de-noising. The second stage is the key to improving the forecasting performance that, adopting the decomposition method, the average fitting performance is improved by 52.84% than the single models. Before the third stage, chaos test is necessary to determine whether there is a requirement of error modification. In summary, the proposed prediction framework can capture the complex characteristics for different short-term hourly wind speed time series, achieving greater performance than the other comparative models.