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Sobre

Sobre

Professr Associado desde 2011 na Faculda de de Engenhgaria da Universidade do Porto (FEUP).

Doutorado em 1995 em Engenharia Eletrotécnica e Computadores na FEUP.

Licenciado em 1984 em Engenharia Eletrotécnica e Computadores na FEUP.

Investigador do INESC TEC desde 1985.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Nuno Fidalgo
  • Cargo

    Investigador Sénior
  • Desde

    25 junho 1985
052
Publicações

2024

Photovoltaic Projects for Multidimensional Poverty Alleviation: Bibliometric Analysis and State of the Art

Autores
Castro L.F.C.; Carvalho P.C.M.; Saraiva J.P.T.; Fidalgo J.N.;

Publicação
International Journal of Energy Economics and Policy

Abstract
Motivated by initiatives such as the UN Sustainable Development Goals (SDG), particularly SDG 1-Poverty Eradication and SDG 7-Clean and Accessible Energy, the search for solutions aiming to mitigate poverty has been recurrent in several studies. This paper main objective is to evaluate the dynamics of global research on the use of photovoltaic projects for poverty alleviation (PVPA) from 2003 to 2022. We use a bibliometric analysis to identify publication patterns and consequently list research trends and gaps of the area. A total of 336 publications from Scopus database are identified and complemented by a state-of-the-art study, where the articles are investigated and classified according to: Business model and financing and evaluation of PVPA results. The results show that PA is often associated with PV power and its application in rural areas. “Biomass” and “application in developing countries” have become a trend. Urban areas application, aiming to reduce poverty, and the need for a synergetic integration of energy and urban planning, to mitigate the risks associated with energy flow and efficiency, are the most relevant gaps identified. Most of the publications focus on macropolicies effects involving PV technology; papers on projects construction and ex-post are not identified.

2023

Estimation of Planning Investments with Scarce Data - comparing LASSO, Bayesian and CMLR

Autores
Fidalgo, JN; Macedo, PM; Rocha, HFR;

Publicação
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.

2023

Easing Predictors Selection in Electricity Price Forecasting with Deep Learning Techniques

Autores
Silva, AR; Fidalgo, JN; Andrade, JR;

Publicação
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

2022

Identification of Typical and Anomalous Patterns in Electricity Consumption

Autores
Fidalgo, JN; Macedo, P;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries' socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

2022

Decision support system for long-term reinforcement planning of distribution networks

Autores
Fidalgo, JN; Azevedo, F;

Publicação
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
The last decade has witnessed a growing tendency to promote deeper exploitation of power systems infrastructure, postponing investments in networks reinforcement. In particular, the literature on smart grids research often emphasizes their potential to defer investments. The study reported in this paper analyses the impact of reinforcement decisions, comparing the long-term costs associated with different network conditions and economic analysis parameters. The results support the conclusion that network reinforcement deferral is not a panacea, as it often generates costly situations in the long-term. The challenge is not to find new ways to postpone investments, but to find the most beneficial criterion to trigger the grid reinforcements actions. Another contribution of the present work is a decision support system to identify the most economical network reinforcement criterion in terms of the peak to capacity ratio.

Teses
supervisionadas

2023

Attention mechanisms to improve forecasting performance

Autor
António Gonçalo Silva Pinto da Cunha

Instituição
UP-FEUP

2023

Dimensionamento de baterias para serviços auxiliares

Autor
Inês Isabel Guia dos Santos

Instituição
UP-FEUP

2023

Estimação probabilística da qualidade de serviço da rede de distribuição

Autor
José Pedro Telo Sanches Branco

Instituição
UP-FEUP

2022

Estudo do Impacto das Alterações Climáticas no Consumo de Energia Elétrica

Autor
Anabela Garcês de Aguiar

Instituição
UP-FEUP

2022

Planeamento de Investimentos na Rede de Distribuição com Base na Técnica Spike and Slab

Autor
Hugo Francisco Rocha Costa

Instituição
UP-FEUP