  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 (GMMN) 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 noise to the 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 GMMN not only fits the probability distribution of
multiple load curves 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.
