Artificial Load Profiles and PV Generation in Renewable Energy Communities Using Generative Adversarial Networks-Reference-Cited by-同舟云学术

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Artificial Load Profiles and PV Generation in Renewable Energy Communities Using Generative Adversarial Networks

Published:2022-06-14 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)
language:
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Author:

Grasso Francesco¹,Garcia Carlos Iturrino¹,Lozito Gabriele Maria¹,Talluri Giacomo¹

Affiliation:

1. University of Florence,Department of Information Engineering,Florence

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9842860/9842863/09843062.pdf?arnumber=9843062

Reference33 articles.

1. Improved training of wasserstein GANs;gulrajani;Adv Neural Inf Process Syst,2017

2. Wasserstein GAN;arjovsky,2017

3. A Cluster Separation Measure

4. Understanding of a convolutional neural network

5. An analytical formula to identify the parameters of the energy-based hysteresis model;scorretti;J Magn Magn Mater,2021

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Smart Solution for Energy Communities: Integrating Demand Response and Unsupervised Learning Evaluation Metrics;2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON);2024-06-25

2. Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers;Applied Energy;2023-12

3. Autoencoders for Hourly Load Profile Reconstruction in Renewable Energy Communities;IEEE EUROCON 2023 - 20th International Conference on Smart Technologies;2023-07-06

4. Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms;IEEE Access;2023

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