使用生成矩匹配网络生成制冷、供暖和电力负荷的场景
Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks
摘要：
冷、热、电负荷情景生成对于综合能源系统的经济运行和稳定性分析具有重要意义。本文提出了一种新的深度生成网络，基于生成矩匹配网络（GMMN）对制冷、制热和电力负荷曲线进行建模，其中自动编码器将高维负荷曲线转换为低维潜变量，最大平均差异表示生成样本与真实样本之间的相似性度量。在训练模型后，通过向GMMN的场景生成器馈送高斯噪声来生成新的场景。与显式密度模型不同，GMMN不需要人为假设负荷曲线的概率分布，具有更强的普适性。仿真结果表明，GMMN不仅能很好地拟合多类负荷曲线的概率分布，而且能准确地捕捉负荷曲线的形状（例如，大峰值、快速斜坡和波动）、频域特性以及冷却、加热和功率负载的时空相关性。此外，生成的样本的能量消耗非常类似于真实样本的能量消耗。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
