基于多头概率稀疏自注意力模型的综合能源系统多元负荷短期预测OACSTPCD

Accurate short-term forecasting of multienergy loads is the basis for the dispatch and operation of integrated energy systems.There is a strong coupling between multiple loads in an integrated energy system,and the existing single load forecasting is challenging to explore the complex internal relationship between multiple loads.Therefore,a short-term forecasting method for multienergy loads based on a multihead probabilistic sparse self-attention(MPSS)model was proposed.First,the Pearson correlation coefficient was used to analyze the correlation between multiple loads,the coupling features between multiple loads were extracted,a multihead probabilistic sparse self-attention mechanism with improved location coding was used to learn the dependencies of long-sequence inputs,and the parameter soft sharing mechanism of multivariate prediction tasks was adopted.The sharing mechanism realizes the joint prediction of multiple loads through a differentiated selection of shared features using different subtasks.Finally,the performance of the proposed model was verified using the multiple-load dataset of the Tempe Campus of Arizona State University.Compared with other forecasting models,the results show that the proposed multivariate load forecasting method can effectively improve forecasting accuracy.