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ERIC ZANGHI is Bachelor of Electrical Industrial Engineering at Centro Federal de Educação Tecnólogica Celso Suckow da Fonseca - CEFET-RJ. Completed M.Sc. and D.Sc degrees in Applied Computer Science in Power Systems, by the Universidade Federal Fluminense - UFF, Niterói, Brazil and recognized in Portugal(UP) as Doctorate in Electrical and Computer Engineering. He was entrepreneur in an Automation company for more than 11 years and has been working in this market since 1996. He has designed, developed and commissioned several SCADA and Protection & Control systems, working on major integrations of the Brazilian Electricity System, Basic Industries and Petroleum Platforms. With a remarkable profile in business prospecting, worked as Regional Sales Manager and Commercial Manager Brazil at GE Digital Energy (2012-2016) in the Energy Automation area. Since 2016, works at INESC TEC Research Center (Portugal) as researcher in Energy Systems(i.e, NexStep, EU-Sysflex e Distribution Management and Training System DMS/DTS) and working on technical-scientifical consulting(i.e., Grid4Water, Telemetry4Water e EPAL Microrredes). Is adjunct professor at Instituto Superior Politécnico Gaya for Renewable Energy Engineering degree. His research interests include Smart Grids, Smart Cities, SCADA Systems, Protection & Control, Power Systems Digitization and Education.



  • Name

    Eric Zanghi
  • Cluster

    Power and Energy
  • Role

    Affiliated Researcher
  • Since

    01st April 2016


NEXTSTEP – Developing future smart secondary substations

Carreira, PJG; Santos, JMM; Pires, L; Ferreira, VGM; Almeno, L; Pinheiro, S; Neves, E; Azevedo, L; Costa, N; Gomes, F; Gouveia, C; Zanghi, E; Pereira, J; Simões, N; Tadeu, A; Coimbra, A; Oliveira, J; Aparício, A;

CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution



Conceptual framework for blockchain-based metering systems

Zanghi, E; Do Coutto, MB; de Souza, JCS;


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.


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

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


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.