Since 2020, COVID-19 has repeatedly arisen around the world, which had a significant impact on global economy and culture. The prediction of epidemic will help to deal with current similar risks that may arise in future. So, this paper proposes hybrid model based particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) AdaBoost algorithm. To ad...

Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), support vector machine (SVM). method first uses VMD to decompose current into intrinsic functions (IMFs) in time-frequency domain, then calculates IMFE according IMFs associated wi...

In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, omni-directional prediction is more scientific than single point or interval prediction. this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), Gaussian process regression (GPR) pro...

The development of photovoltaic (PV) power forecast technology that is accurate utmost importance for ensuring the reliability and cost-effective functioning system. However, meteorological factors make solar energy have strong intermittent random fluctuation characteristics, which brings challenges to prediction. This work proposes, a new ultra-short-term PV prediction using an improved sparro...

In this paper, a deep learning-based prediction model VMD-GLRT is proposed to address the accuracy problem of service load prediction. The combines Variational Mode Decomposition (VMD) and GRU-LSTM. At same time, incorporates residual networks self-attentive mechanisms improve model. VMD part decomposes original time series into several intrinsic mode functions (IMFs) part. other uses GRU-LSTM ...

This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the into multiple subseries where each contains unique local characteristics, all are converted two-dimensional samples. (b) A gated recurrent unit (GRU) sequentially mode...

Improving short-term wind speed prediction accuracy and stability remains a challenge for forecasting researchers. This paper proposes new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal spatial correlations speed. First, VASTN is hybrid model combines VMD, squeeze-and-excitation (SENet), attention mechanism (AM)-...