With the rapid penetration of photovoltaic (PV) generation into modern power grids, accurate and robust ultra-short-term PV power forecasting is increasingly important for real-time dispatch and frequency regulation. However, PV power series are volatile, nonlinear, and uncertain at short time scales, challenging conventional methods. This paper proposes a hybrid ultra-short-term forecasting framework that integrates secondary decomposition with advanced learning models. First, key features are screened and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes PV power into intrinsic mode functions (IMFs). Sample entropy quantifies IMF complexity, and K-means clusters IMFs into high- and low-frequency components. High-frequency components are further decomposed by Black-winged Kite Algorithm (BKA)-Variational Mode Decomposition (VMD) to enhance stationarity and reduce manual parameter tuning. The resulting high-frequency sub-signals are predicted using Online Kernel Extreme Learning Machine (OKELM), while low-frequency components are modeled by a Convolutional Neural Network (CNN)-Echo State Network (ESN) to capture spatiotemporal patterns. Final ultra-short-term forecasts are obtained via additive reconstruction. Experiments on datasets from the Ningxia PV station (China) and the Desert Knowledge Australia (DKA) Solar Energy Centre achieve Formula: see text values of 99.6987% and 99.0635% in comparative and validation experiments, respectively, demonstrating high accuracy across different geographic locations and seasons. Improved PV power forecasting reduces uncertainty, supports grid stability, enables more efficient dispatch and reserve scheduling, and lowers operating costs and curtailment.
Xue et al. (Tue,) studied this question.