Jul 2026· Ingegneria sismica· Vol 42, pp. 1-15· 0 citations
Abstract
A VMD-XGBoost ensemble method for enhancing the accuracy of ultra-short-term wind power forecasting at 2-4 hour horizons. The original power series is decomposed into intrinsic mode functions by variational mode decomposition (VMD), and separate XGBoost models, with lagged features, are built for each component, with final forecasts obtained by summation. Using 3.3 years of 15-min wind farm data, the proposed method achieves an R2 of 0.8922 and an RMSE of 21.815 MW at the 4-h horizon, outperforming raw XGBoost by 18.1% in R2 and 33.6% in RMSE. Inference time remains below 0.05 s, confirming real-time applicability.
Accurate wind forecasting is critical to ensure stable and efficient integration of renewable energy resources in modern power systems. However, the inherent variability and non-stationarity of wind pose a significant forecasting problem for modern power system operators to ensure power system stability. A new hybrid f...
Heshan Senapriya, Sakun Rasilka, D. P. Wadduwage· Moratuwa Engineering Researc...· 0 citations
A multi-site wind power forecasting system based on power decomposition and deep model ensemble that applies Variational Mode Decomposition (VMD) to separate raw power sequences into high-frequency and low-frequency components, each directed into a structurally symmetric dual-branch framework.
To address the issues of systematic bias and non-stationarity in wind power forecasting, this paper proposes a bias-corrected Transformer-GRU hybrid forecasting model optimized by Variational Mode Decomposition (VMD) and the Differential Evolution (DE) algorithm. First, VMD is employed to adaptively decompose the origi...
Sen Wang· Sixth International Conferen...· 0 citations
With the rapid growth of wind power penetration, the inherent randomness and uncertainty of wind power pose serious challenges to the stable operation of power systems. To address this issue, this paper proposes a wind power forecasting model based on dual decomposition. The model first applies Variational Mode Decompo...
Rui Huang, Jia-Yi Li, Ying-Ying Wang et al.· European Conference on Elect...· 0 citations
In response to the challenges of ultrashort-term wind power forecasting, such as strong nonstationarity, multiscale dynamic features, and sensitivity of model hyperparameters, this paper proposes a novel forecast framework integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMD...
Yi-Ming Jin, Hao-Yu Wu, Jian-Fei Chen· Journal of Energy Engineerin...· 0 citations
To address the strong non-stationarity, coupled multi-scale fluctuations, and insufficient parameter adaptability of net load forecasting models under high photovoltaic penetration, an ICEEMDAN-BO-BiGRU combined short-term net load forecasting model is proposed. First, the net load sequence is constructed from the actu...
Qiang Wang, Hao-Yang Li, Tian-Yu Song et al.· Processes· 0 citations
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