基于数据驱动的用户用能行为分析方法

随着综合能源系统的快速发展，为了满足综合能源系统联合规划的需要，亟需对综合能源系统中用户用能行为进行分析建模。基于数据驱动思想，引入了深度学习方法，提出了一种用于用户用能行为分析的方法。首先，对影响用户用能行为的数据类型、结构进行分析；然后，引入Seq2Seq技术，以GRU为神经元构建深度学习模型；最后，通过算例对所提方法的有效性进行验证。算例研究表明:所提的方法能够以海量历史数据为基础，准确预测出用户的用能行为情况。

Analysis Method of Data-driven Energy Use Behavior of Users

With the rapid development of integrated energy system，it is urgent to analyze and model the energy use behavior of users in order to meet the needs of integrated energy system joint planning. Based on the idea of data-driven，deep learning method is introduced， and analysis method for energy use behavior of users is proposed. Firstly，the data types and structures that affect energy use behavior of users are analyzed. Then， Seq2Seq technology is introduced to construct a deep learning model with GRU as the neuron. Finally，the effectiveness of the proposed method is verified by an example. The example shows that the proposed method can accurately predict the energy use behavior of users based on massive historical data.