Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Paulos
  • Cluster

    Energia
  • Cargo

    Investigador
  • Desde

    20 setembro 2017
008
Publicações

2021

Non-Intrusive Load Monitoring for Household Disaggregated Energy Sensing

Autores
Paulos J.P.; Nuno Fidalgo J.; Gama J.;

Publicação
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Abstract

2021

Detection and Mitigation of Extreme Losses in Distribution Networks

Autores
Paulos J.P.; Fidalgo J.N.; Saraiva J.T.; Barbosa N.;

Publicação
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Abstract

2018

Load and electricity prices forecasting using Generalized Regression Neural Networks

Autores
Paulos, JP; Fidalgo, JN;

Publicação
2018 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST)

Abstract
Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.