Demand Response Potential Prediction Based on Improved Conditional Generative Adversarial Networks
In order to solve the problems such as the dependence of the existing various baseline load estimation methods on temporality and the inaccurate assessment of the classification analysis of multiple influencing factors,a user-side flexibility resource response potential assessment method based on the improved conditional generative adversarial networks(CGAN)and graph convolutional neural networks(GCN)is proposed.Firstly,a fusion of wasserstein generative adversarial networks(WGAN)and CGAN is introduced to create.Wasserstein conditional generative adversarial networks(WCGAN),and historical data are used to train the generator and discriminator to estimate the baseline load;then,with full consideration of the uncertainty of the baseline and response loads,we propose an approach based on the WCGAN and GCN;finally,the effectiveness of the proposed method is analysed by using actual load data.
NETL
NSTL
万方数据
