摘要
综合能源系统的多元负荷短期预测,对系统的优化调度和经济运行至关重要。多元负荷之间耦合关系紧密,Transformer作为一种完全建立在自注意力机制上的模型,能很好地分析多元负荷之间的内在联系。传统Transformer模型针对自然语言类问题而设计,难以直接应用于多元负荷预测。为此,提出一种GRU-TGTransformer(GRU-Talkinghead-Gated residuals-Transformer)模型。该模型采用门控循环单元代替原有的词嵌入及位置编码环节,对输入数据进行特征融合,取得具备相对位置信息的高维特征数据。通过在多头自注意力环节引入交流机制,提高多头自注意力的表达效果。为进一步强化网络结构,在残差连接中引入门控单元,提高模型在时序预测问题上的稳定性。以美国亚利桑那州立大学坦佩校区的综合能源系统为算例,通过对所提出模型与传统模型之间进行对比分析,证明所提出的模型具有更高的预测精度。
The multi load short-term forecast is very important for the optimal scheduling and economic operation of an integrated energy system.There are strong coupling relationships among the multi loads,and the model structure of the transformer is completely based on the self-attention mechanism.This can better analyze the internal relationship between multi loads.It is difficult to directly apply the traditional transformer in multi load forecasting because it is designed for natural language processing problems.For this reason,this paper proposes a GRU-Talkinghead-Gated residuals-Transformer(GRU-TGTransformer)model,which uses gated recurrent units,instead of the original word embedding and position coding links,to extract the input features of input data and obtain high-dimensional feature data with relative position information.By introducing a communication mechanism in the multi-head self-attention,the self-attention expression effect is improved.A gate unit is introduced into the residual connection to improve the stability of the model in time series prediction.This paper uses the integrated energy system of Arizona State University's Tempe campus as an example to prove that the proposed model has higher prediction accuracy than the traditional model.
作者
李云松
张智晟
LI Yunsong;ZHANG Zhisheng(College of Electrical Engineering,Qingdao University,Qingdao 266071,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2023年第15期33-41,共9页
Power System Protection and Control
基金
国家自然科学基金项目资助(52077108)。
关键词
综合能源系统
多元负荷预测
短期
多头交流自注意力
门控残差
integrated energy system
multi load forecasting
short-term
talking-head self-attention
gated residuals
