基于综合相关性指标与SA-BiGRU的综合能源系统多元负荷预测

Multi-Energy Load Forecasting in Integrated Energy Systems Based on Comprehensive Correlation Index and SA-BiGRU Network

短期负荷预测为综合能源系统安全稳定运行提供保障,但负荷波动的不确定性及多种能量相互耦合增大了预测难度。基于此,提出一种基于综合相关性指标和SA-BiGRU的综合能源系统多元负荷预测模型。考虑到不同气象因素对多元负荷的影响,采用综合相关性指标计算气象因素与负荷间的相关性,提出多元负荷三项耦合乘积挖掘能源间交叉耦合关系,并构建特征矩阵作为预测模型输入。同时,利用自适应k-means将原始输入数据划分为不同负荷场景,降低预测复杂度;在双向门控循环单元网络中引入自注意力机制,为输入特征赋予不同权重,从而增强模型对重要特征的区分能力。最后,采用算例与现有模型进行对比分析,结果表明所提出的多元负荷预测方法具有更高的预测精度和更短的预测时间。

Short-term load forecasting provides a guarantee for the safe and stable operation of IES,but the uncertainty of load fluctuations and the coupling of multiple energy sources increase the difficulty of prediction.To address this,this paper proposes a multi-dimensional load forecasting model for the integrated energy system based on the comprehensive correlation index and SA-BiGRU.Firstly,the comprehensive correlation index is used to calculate the correlation between meteorological factors and loads,and a multi-dimensional load coupling feature matrix is constructed to explore the cross-coupling relationship between energy sources,then the coupled load features are constructed as model inputs.At the same time,It divides the input into different load scenarios using adaptive k-means clustering,in order to reduce modeling complexity.And a self-attention mechanism is incorporated into the bidirectional GRU network to differentiate the importance of input features for enhanced distinction.Finally,compared with other forecasting models,the results show that the proposed method has higher accuracy and shorter forecasting time.