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About

About

Associate Professor since 2011 at the Faculty of Engineering of the University of Porto (FEUP).

PhD obtained in 1995 in Electrical Engineering and Computers at FEUP.

Licenciado in 1984 in Electrical Engineering and Computers at FEUP.

Researcher at INESC TEC since 1985.

Interest
Topics
Details

Details

  • Name

    José Nuno Fidalgo
  • Role

    Senior Researcher
  • Since

    25th June 1985
048
Publications

2023

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

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

Publication
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

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

Publication
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

Authors
Fidalgo, JN; Macedo, P;

Publication
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

Authors
Fidalgo, JN; Azevedo, F;

Publication
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.

2022

The Value of Investments in Network Efficiency in Systems with a Large Integration of Distributed Renewable Generation

Authors
Fidalgo, JN; Paulos, JP; MacEdo, P;

Publication
International Conference on the European Energy Market, EEM

Abstract
This article analyzes the effects of the current policy trends - high levels of distributed generation (DG) and grid load/capacity ratio - on network efficiency. It starts by illustrating the network losses performance under different DG and load/capacity conditions. The second part concerns the simulation of network investments with the purpose of loss reduction for diverse system circumstances, including the impact of DG levels, energy cost, and discount rate. The attained results showed that DG, particularly large parks, have a negative impact on network efficiency: network losses tend to intensify with DG growth, under the current regulation. Furthermore, network investments in loss reduction would have a small global impact on network efficiency if the DG parks' connection lines are not included in the grid concession (not subjected to upgrade). Finally, the study determines that it is preferable to invest sooner, rather than to postpone the grid reinforcement for certain conditions, namely for low discount rates. © 2022 IEEE.

Supervised
thesis

2022

Towards Biometrically-Morphed Medical Case-based Explanations

Author
Maria Manuel Domingos Carvalho

Institution
UP-FEUP

2022

Immersive 2D Scatterplots and Parallel Coordinates for Information Visualization in a Virtual Reality Environment

Author
Paulo Jorge Palhau Moutinho

Institution
UP-FEUP

2022

Estudo do Impacto das Alterações Climáticas no Consumo de Energia Elétrica

Author
Anabela Garcês de Aguiar

Institution
UP-FEUP

2022

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

Author
Hugo Francisco Rocha Costa

Institution
UP-FEUP

2022

Comparação de plataformas low-code

Author
Mariana Gonçalves Marques

Institution
UM