Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Sobre
Download foto HD

Sobre

Engenheiro Industrial Elétrico pelo faculdade federal CEFET-RJ em 1997, Mestre em Computação Aplicada em Sistemas de Potência e cursando Doutoramento na mesma linha de investigação.

Com mais de 22 anos de experiência no mercado de automação, iniciando em sistemas científicos e depois atuando em Automação Industrial, Óleo&Gás e Sistemas de Energia.
Projetou, Desenvolveu e Comissionou diversos sistemas SCADA e de Proteção & Controle, atuando em importantes obras de integrações do Sistema Elétrico Brasileiro, industrias de Base e em Plataformas de Petróleo. Ministrou Treinamentos e Palestras para empresas e instituições da área acadêmica.

Foi empresário de uma Integradora de Sistemas de Automação(AGNUS Tecnologia Eletrônica) por mais de 10 anos sendo Gerente de Engenharia, Diretor Comercial e Administrador, atuando no mercado mundial de Automação de Sistemas Industriais e de Energia.

Com perfil marcante em prospecção de negócios, em 2012 ocupou o cargo de Gerente Regional de Vendas de soluções de Automação de Subestações e Energia na empresa GE Digital Energy do Brasil e depois de 3 anos, ocupou o cargo de Gerente Comercial Brasil na área de GE Grid Automation até 2016.

Desde abril de 2016, atua como investigador no centro de pesquisas INESC TEC (Portugal) na divisão de energia (CPES), trabalhando em Smart Grids, Dispatch Training Simulator e em suporte na prospeção de novos negócios. Atua hoje nos projetos DTS/DMS(Efacec) e NextStep.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Eric Zanghi
  • Cluster

    Energia
  • Cargo

    Investigador
  • Desde

    01 abril 2016
005
Publicações

2019

Conceptual framework for blockchain-based metering systems

Autores
Zanghi, E; Do Coutto Filho, MB; Stacchini de Souza, JCS;

Publicação
MULTIAGENT AND GRID SYSTEMS

Abstract
The smart grid environment requires the enhancement of various computational tools, especially for routine tasks of data acquisition and system monitoring. This paper presents the building blocks of a conceptual framework to be used as the basis for the construction of novel distributed remote metering systems with utilization of the cutting edge Blockchain technology. The proposed methodology is suitable for processing a large volume of data aimed at monitoring modern electric power distribution grids. As a proof of concept, a collaborative metering system is conceived based on the Blockchain technology, being primarily capable of: dealing with the entirety of the collected data (conveniently stored and filtered); assuring data integrity by means of cryptography; optimizing implementation/operation costs of the telecommunication services involved. Simulation results concerning the reliability and performance of the designed distributed remote metering system are presented.

2018

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Autores
de Miranda, ST; Abaide, A; Sperandio, M; Santos, MM; Zanghi, E;

Publicação
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS

Abstract
Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.