开展多元负荷的准确预测对提高新能源消纳、实现节能减排、确保电网安全可靠运行具有重要意义。为了提高多元负荷同时预测的精度，构建了奇异谱分析与双向长短期记忆网络相结合的多元负荷同时预测模型。首先，利用皮尔逊相关系数进行耦合特征提取，以识别多元负荷数据中的内在关联和依赖关系；其次，使用奇异谱分析进行特征提取，以便更全面地捕捉多元负荷数据的动态特性，降低预测难度。最后，针对所提模型引入多任务学习，利用多个负荷预测任务之间的共享信息，相互辅助进行预测，提升预测精度。实验分别通过多区域多元负荷和柔性负荷及风光发电数据进行仿真分析，结果表明，在多区域中电、热、冷负荷预测平均绝对百分比误差平均提高0.41%，均方根误差平均提高0.02 MW。

It is of great significance for accurate forecasting of multi-load to be carried out to improve the consumption of new energy, realize energy saving and emission reduction, and ensure the safe and reliable operation of the power grid. To enhance the accuracy of simultaneous multi-load forecasting, a model which singular spectrum analysis and bi-directional long short-term memory networks SSA-BiLSTM (singular spectrum analysis-bidirectional long short-term memory) was proposed. First, A approach Pearson correlation coefficients for coupled feature extraction was proposed to identify correlations and dependencies within multivariate load data. Then, SSA was employed for feature extraction to capture dynamic characteristics and reduced forecasting complexity. Finally, a multi-ask learning framework was introduced to leverage shared information among multiple forecasting tasks, improving prediction accuracy. Experimental using datasets from multi-area electricity, heat, cold multivariate loads, flexible and wind-solar power generation, the effectiveness of the model. The results show that the proposed model average improves in mean absolute percentage error (MAPE) for the prediction of electrical, heating, and cooling loads in multiple regions is 0.41%, with an average root mean square error (RMSE) increase of 0.02 MW.