Ultra-Short-Term Wind Power Forecasting Using a Two-Stage Signal Decomposition and iTransformer-LSTM-KAN Hybrid Framework

Medium and Short-Term Wind Power Forecasting Using LCNN-Mamba Model Based on Quadratic Decomposition

Accurate wind power forecasting is essential for advancing sustainable wind energy utilization, stabilizing new energy power systems, and facilitating the low-carbon and sustainable development of the power industry. Aiming to address the challenges of strong non-stationarity, significant multiscale fluctuations, and the difficulty in jointly modeling local dynamics and long-term dependencies in wind power time series, this study proposes a short-to-medium-term wind power forecasting method based on multiscale decomposition and local-global cooperative modeling. First, Seasonal Trend Decomposition (STL) is employed to extract the trend and seasonal components from the raw time series. The residual series is then subjected to Adaptive Noise-Complemented Empirical Mode Decomposition (CEEMDAN) to reveal the underlying non-stationary characteristics. Based on the dominant frequency characteristics, the decomposed components are reconstructed into high, medium, and low-frequency series, which, together with the trend and seasonal components, form a multidimensional set of input features. Second, to accommodate the time-series characteristics of the multiscale components, we designed an LCNN-Mamba forecasting framework that integrates a Local Convolutional Neural Network (LocalCNN) with a state-space model. The LocalCNN is used to model local and multiscale dynamic features, while the multi-layer Mamba state-space module efficiently captures long-term time-series dependencies, thereby achieving a synergistic enhancement of both local sensitivity and global modeling capabilities. The proposed model was validated using actual operational data from wind farms at two different locations. Experimental results show that the proposed approach outperforms traditional forecasting methods in terms of forecasting accuracy, providing a reliable technical reference for wind power grid-connected operation, power dispatching optimization, and the large-scale and high-quality sustainable development of wind energy resources.

Ultra-Short-Term Wind Power Forecasting Integrating Multi-Scale Decomposition and Dual Dependency Interaction

Accurate wind power forecasting is an essential prerequisite for ensuring the safe and stable operation of power systems and improving the scheduling and planning capability of power grids. To address the large prediction errors caused by the strong nonlinearity and non-stationary fluctuations of wind power time series, as well as the coupling effects among multiple meteorological variables, this paper proposes a wind power forecasting model integrating multi-scale decomposition, dual dependency interaction, and cross-variable linear mapping. The multi-scale decomposition module employs multi-scale average pooling to separate the trend and periodic components of the sequence, thereby effectively mitigating the non-stationary interference of the original series. The dual dependency interaction mechanism explores long-term temporal correlations and coupling relationships among meteorological factors from both temporal and variable dimensions. Finally, a cross-variable linear structure is adopted to accomplish prediction. Experiments are conducted using annual measured data collected from a wind farm in Inner Mongolia, China. The proposed model is compared with several mainstream forecasting models, including Informer, xLSTM-Informer, GRU, and CNN-LSTM. Experimental results demonstrate that the proposed model achieves MSE, RMSE, MAE, and R2 values of 10.32, 3.214, 1.966, and 0.961, respectively. Compared with the xLSTM-Informer model with the best overall baseline performance, the proposed model reduces MAE by 7.35% and cuts training time by 97.51%, thus conclusively demonstrating that the proposed method achieves much better forecasting accuracy without sacrificing training efficiency.

Multi-site Wind Energy Prediction System Based on Power Decomposition and Deep Model Integration

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.

Hybrid Short-Term Wind Power Forecasting Using EEMD and Feature- Temporal Attention-Based TCN-BiLSTM Network

To improve short-term wind power forecasting accuracy and interpretability, this paper proposes an EEMD-FTA-TCN-BiLSTM model, where FTA denotes feature-temporal attention. The method first uses Ensemble Empirical Mode Decomposition (EEMD) to adaptively decompose the non-stationary power sequence into high-, medium-, and low-frequency components, representing short-term fluctuations, periodic variations, and long-term trends. TCN extracts local temporal patterns through causal and dilated convolutions, while BiLSTM captures bidirectional temporal relationships within the historical input window. A feature-temporal attention mechanism dynamically fuses key meteorological and historical power features. LightGBM then learns and corrects systematic residual errors in the preliminary forecasts. Pearson correlation analysis is used to provide interpretable feature analysis and preliminary feature screening. Validated with one year of data from a single wind farm in Inner Mongolia, the model achieves lower forecasting errors than the compared benchmark models and shows consistent forecasting performance across the meteorological conditions examined within the studied site. The ablation results support the contributions of the evaluated components. The proposed framework improves forecasting accuracy and interpretability for single-site short-term wind power forecasting, while multi-site spatial dependency modeling is left for future work.

Short-term wind power forecasting with lag-based feature engineering: a turbine-level machine learning comparison

Ultra-Short-Term Photovoltaic Power Forecasting Based on a Hybrid Decomposition Linear Long Short-Term Memory Model

The rapid expansion of photovoltaic (PV) systems poses significant challenges to grid stability. Hybrid Energy Systems (HES) are intended to alleviate this volatility, yet their coordinated dispatch often remains suboptimal due to communication delays and ramp-rate constraints. Accurate ultra-short-term PV power forecasting is therefore essential, as it enables preemptive control and timely dispatch adjustments that unlock the full potential of HES. In this study, we propose a novel AI hybrid forecasting framework that integrates a rule-based model with a Decomposition Linear (DLinear) Long Short-Term Memory (LSTM) deep learning core, representing, to the best of our knowledge, a novel integration of a decomposition-based linear model (DLinear) with LSTM networks for ultra-short-term PV power forecasting. The DLinear component decomposes the time series into trend and remainder sequences, which are then independently modeled by separate LSTM networks to capture distinct dynamics. Using data from a 300 kWp PV power station, the framework achieves an average daily prediction accuracy exceeding 93% for both 5-min and 15-min horizons. The model reliably tracks power variations under sunny and rainy conditions, while under volatile cloudy weather its accuracy decreases but still captures essential fluctuation patterns. These results demonstrate the potential of the proposed framework for improving the dispatch and operational reliability of hybrid energy systems. However, further validation across additional seasons and sites is needed to establish broader generalizability.