Scenario generation for cooling, heating, and power loads using generative moment matching networks

fault location in power distribution networks using matching algorithm

چکیده رساله/پایان نامه : تاکنون روش‏های متعددی در ارتباط با مکان یابی خطا در شبکه انتقال ارائه شده است. استفاده مستقیم از این روش‏ها در شبکه توزیع به دلایلی همچون وجود انشعاب‏های متعدد، غیر یکنواختی فیدرها (خطوط کابلی، خطوط هوایی، سطح مقطع متفاوت انشعاب ها و تنه اصلی فیدر)، نامتعادلی (عدم جابجا شدگی خطوط، بارهای تک‏فاز و سه فاز)، ثابت نبودن بار و اندازه گیری مقادیر ولتاژ و جریان فقط در ابتدای...

Generative Moment Matching Networks

We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer preceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network, however, requires careful optimization of a difficult minimax program. Inst...

A Heuristic for Moment-Matching Scenario Generation

Conditional Generative Moment-Matching Networks

Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment-matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learn...

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from...