Scenario generations of cooling

heating

and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper

a novel deep generative network is proposed to model cooling

heating

and power load curves based on generative moment matching networks (GMMNs) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model

the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models

the proposed GMMN does not need to artificially assume the probability distribution of the load curves

which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves very well

but also accurately captures the shape (e.g.

large peaks

fast ramps

and fluctuation)

frequency-domain characteristics

and temporal-spatial correlations of cooling

heating

and power loads. Furthermore

the energy consumption of generated samples closely resembles that of real samples.