1 research outputs found
Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks
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 a 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 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 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.Comment: This paper has been accepted by CSEE Journal of Power and Energy
System