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About

About

Industrial Electrical Engineer by Federal Faculty CEFET-RJ in 1997, Master in Applied Computing in Power Systems and attending PhD in the same line of research.

More than 22 years of experience in the automation market, starting in scientific systems and later acting in Industrial Automation, Oil & Gas and Energy Systems.
Designed, developed and commissioned several SCADA and Protection & Control systems, working on important projects about the integration of the Brazilian Electrical System, base Industries and Petroleum Platforms.
Ministered Training and Lectures for companies and academic institutions.

Was entrepreneur in a Automation Systems Integration company(AGNUS Tecnologia Eletrônica) for more than 10 years as Engineering Manager, Commercial Director and Administrator, working in the world market of Industrial Systems and Energy Automation.

With a strong profile in business prospecting, in 2012 he held the position of Regional Sales Manager for Substation and Energy Automation solutions at GE Digital Energy do Brasil and after 3 years, he held the position of Commercial Manager Brasil in the GE Grid Automation until 2016.

Since April 2016, works as researcher at the INESC TEC research center (Portugal) in the energy division (CPES), working on Smart Grids, Dispatch Training Simulator and support in Business prospection. Currently working on DTS / DMS (Efacec) and NextStep projects.

Interest
Topics
Details

Details

  • Name

    Eric Zanghi
  • Cluster

    Power and Energy
  • Role

    Research Assistant
  • Since

    01st April 2016
Publications

2018

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

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

Publication
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.