A Domain-Adaptive and Attention-Guided Method for Extracting Cross-Scenario Energy Scheduling Rules

Abstract

Accurate meteorological forecasts are crucial for optimizing wind-solar scheduling systems, but increasing climate anomalies have led to significant changes in the statistical distribution of meteorological data over time. Conventional deep learning models struggle with these non-stationary distributions. To address this, we propose the DGA-DANN-TCN-LSTM framework. This domain-adaptive approach incorporates a domain-guided attention (DGA) mechanism, utilizing a global mean query to align source and target feature distributions through adversarial training to effectively capture overall climate contexts.Using ERA5 reanalysis data for the Jinsha River basin, experiments demonstrate the proposed model significantly outperforms the TCN-LSTM and DANN-TCN-LSTM. Under extremely sparse data conditions with only 30 days of training data, the DGA mechanism successfully captured seasonal patterns. Compared to the TCN-LSTM model, wind speed prediction R² improved by 91.8%, and the root mean square error decreased by 49.7%. By compensating for the standard DANN model’s neglect of global seasonal trends, the proposed model achieves prediction accuracy comparable to the standard DANN-TCN-LSTM model trained on 365 days of data when trained with only 90 days of data. Under full-year data conditions, this model still reduces the root mean square error by 10% to 33% compared to the TCN-LSTM model. The proposed model realizes the high accuracy prediction of multivariate meteorological data across time domains, and provides an effective method for water-wind-photovoltaic complementary scheduling.