一种集成多源数值天气预报的短期非参数概率光伏功率预测两阶段集成学习框架

Utilizing numerical weather prediction (NWP) for short-term solar power probabilistic forecasting (SSPPF) has proven to be an effective approach to enhance the integration and utilization of solar energy. However, most existing SSPPF studies only employ single-source NWP, overlooking the potential benefits of multi-source NWPs for improving the accuracy and robustness of probabilistic forecasting. This paper proposes an improved two-stage ensemble learning forecasting framework (ITS-ELFF) for SSPPF. ITS-ELFF utilizes multi-source NWPs as key exogenous covariates to generate multi-step quantile forecasts. In the first stage, a set of robust and diverse base learners provides the initial quantile forecasts. In the second stage, a meta-learner integrates the quantile forecasts from all base learners to generate the final predictive quantiles. To explore the valuable information in multi-source NWPs, we introduce skip connections in the ITS-ELFF and incorporate a feature attention mechanism to dynamically select critical features. Furthermore, a multi-head temporal attention mechanism is incorporated into the meta-learner to effectively capture hidden long-term temporal dependencies within the solar power time series. A non-crossing quantile generation strategy is proposed to ensure the monotonicity of the predictive quantiles. Finally, a two-stage training strategy is developed for ITS-ELFF to avoid overfitting. Numerical results on public real-world data verify the effectiveness of the proposed ITS-ELFF in SSPPF.