Abstract. In the last few years, wind power forecasting has established itself as an essential tool in energy industry due to increase of penetration electric grid. This paper presents a method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed decompose time series data into several intrinsic functions residual component. Afterwards, every function traine...

The carbon dioxide (CO2) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected improve inversion result. In this paper, we simultaneously decompose three pairs into different intrinsic mode functions (IMFs) using of ensemble empirical decomposition (EEMD). Further, co...

Empirical mode decomposition (EMD) is a data-driven and multi-scale transform theory, and it is a nonlinear and non-stationary signal processing theory. But each EMD decomposition theory has its advantages and disadvantages. Synthetic aperture radar (SAR) imaging is an important remote sensing technique to obtain the change information, and SAR image data belongs to non-stationary signal. So EM...