基于条件时序生成对抗网络的楼宇空调负荷场景生成方法

Scenario Generation of Air Conditioning Loads in Buildings Based on Conditional TimeGAN

电力系统运行与规划中需重点关注到楼宇空调负荷的不确定性,可将楼宇空调负荷变化的不确性场景转化为多个确定性场景的场景生成问题。提出了楼宇空调负荷场景生成问题的基本分析框架,深入分析了楼宇空调负荷的用能特征,挖掘了楼宇空调负荷用能时序序列数据所蕴含的动静态特征。将楼宇空调负荷数据的动静态特征作为条件监督项,将无监督对抗训练与监督训练相结合,设计了联合训练损失函数与全局优化损失函数,并在此基础上提出了一种基于条件时序生成对抗网络(time series generative adversarial nets,TimeGAN)的楼宇空调负荷场景生成方法。最后,通过算例验证了所提方法的可行性与有效性。研究成果对提高楼宇空调负荷主动参与电力系统的运行规划有积极的意义。

The uncertainty of the air conditioning loads in buildings should be paid more attention to in the power system operation and planning.The uncertain scenarios of the airconditioning loads in buildings can be transformed into a scenario generation problem of multiple deterministic scenarios.The basic analytic framework of the building air conditioning load scenario generation is put forward in this paper.The energy consumption characteristics of the building air conditioning loads are deeply analyzed,and the dynamic and static characteristics contained in the energy consumption time series data of the building air conditioning loads are excavated.Taking the dynamic and static characteristics of the building air conditioning load data as the conditional supervision items,the joint training loss function and the global optimization loss function are designed by combining the unsupervised confrontation training with the supervised one.On this foundation,a building air conditioning load scene generation based on the conditional TimeGAN(Time Series Generative Adversarial Nets)is proposed.Finally,an example is given to verify the feasibility and effectiveness of the proposed method.This study is of positive significance for the air conditioning loads in buildings actively participating in the operation planning of the power system.