Hybrid Short-Term Wind Power Forecasting Using EEMD and Feature- Temporal Attention-Based TCN-BiLSTM Network
TL;DR
An EEMD-FTA-TCN-TCN-BiLSTM model, where FTA denotes feature-temporal attention, which achieves lower forecasting errors than the compared benchmark models and shows consistent forecasting performance across the meteorological conditions examined within the studied site.
Abstract
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.