COMBINING PROBABILISTIC PREDICTION OF PV POWER BASED ON SELF-ATTENTION FEATURE EXTRACTION MECHANISM
To accurately predict the probability distribution of photovoltaic output,this paper proposes a novel approach for probabilistic forecasting of photovoltaic power based on self-attention feature extraction mechanism.This method addresses the limitations of traditional linear combining prediction methods in terms of information utilization and flexibility.Firstly,a model pool is constructed which includes both homogeneous models and heterogeneous models.Subsequently,a feature extraction module based on self-attention mechanism is employed to adaptively extract features from the predictions of the base models.Lastly,the extracted features from the base models and the external features are input into a residual fully-connected network to achieve monotonic quantile prediction through quantile increment prediction.Experimental analysis using publicly available photovoltaic datasets demonstrates that the proposed method outperforms individual models and traditional linear combination methods in terms of overall performance.
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