Understanding the electrical behavior of consumption and generation in residential areas featuring distributed photovoltaic generation is an important asset for both distribution companies and communities. This analysis is often in statistical terms. However, load and generation data can be noisy, sparse and irregular, with resulting difficulties in the following statistical analysis. A generative adversarial machine learning approach can be used to create artificial data with meaningful stochastic properties. This work focuses on the use of DCWGAN to create different profiles taken from multiple data clusters of residential sectors. This approach successfully extracts the statistical properties of the dataset and creates profiles based each on cluster with a given randomness. The implemented neural system is a powerful tool that can be used to create datasets useful for power flow analysis, optimal grid management and economic revenue simulation.

Artificial Load Profiles and PV Generation in Renewable Energy Communities Using Generative Adversarial Networks / Grasso, F; Iturrino Garcia, C; Lozito, GM; Talluri, G. - ELETTRONICO. - (2022), pp. 709-714. ((Intervento presentato al convegno 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) [10.1109/MELECON53508.2022.9843062].

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

Grasso, F;Iturrino Garcia, C;Lozito, GM;Talluri, G
2022

Abstract

Understanding the electrical behavior of consumption and generation in residential areas featuring distributed photovoltaic generation is an important asset for both distribution companies and communities. This analysis is often in statistical terms. However, load and generation data can be noisy, sparse and irregular, with resulting difficulties in the following statistical analysis. A generative adversarial machine learning approach can be used to create artificial data with meaningful stochastic properties. This work focuses on the use of DCWGAN to create different profiles taken from multiple data clusters of residential sectors. This approach successfully extracts the statistical properties of the dataset and creates profiles based each on cluster with a given randomness. The implemented neural system is a powerful tool that can be used to create datasets useful for power flow analysis, optimal grid management and economic revenue simulation.
2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)
2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)
Grasso, F; Iturrino Garcia, C; Lozito, GM; Talluri, G
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1283054
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact