Jose Nuno Moura Marques Fidalgo

@fe.up.pt

Department of Electrical and Computer Engineering / Faculty of Engineering of Porto
University of Porto

Jose Nuno Moura Marques Fidalgo


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https://researchid.co/jfidalgo

RESEARCH INTERESTS

Power systems, renewable energy, artificial intelligence, forecasting
58

Scopus Publications

Scopus Publications

  • Feature-engineered long-term hourly load forecasting with climatic uncertainty integration
    José Pedro Paulos, Filipe Azevedo, J. Nuno Fidalgo
    Electric Power Systems Research, 2026
  • Analysis and Optimization of Battery Energy Storage Systems in Energy Markets
    Gonçalo Baptista, J. Nuno Fidalgo
    International Conference on the European Energy Market Eem, 2025
    This article explores the optimization of Battery Energy Storage Systems (BESS) in energy markets, emphasizing their role in decarbonization by storing excess renewable energy and mitigating grid constraints. BESS enables energy transition by facilitating energy arbitrage, frequency regulation, and grid stabilization, essential for integrating variable renewable sources. Focusing on the UK energy market, the study highlights the favorable policies and investments driving BESS deployment. It examines revenue streams, including Day-Ahead and Intraday markets, ancillary services, and balancing mechanisms, particularly dynamic services like frequency regulation. Challenges such as gas market volatility and regulatory hurdles are also discussed. The proposed market optimization model simulates BESS operations, revealing consistent revenue potential influenced by market conditions and regulatory frameworks. The study underscores BESSs critical role in stabilizing grids, supporting renewables, and enhancing energy security while calling for further research on equipment degradation and broader impacts on energy systems and pricing.
  • Probabilistic Estimation of the Quality-Of-Service Indexes in Distribution Networks
    José Pedro T.S. Branco, Pedro Macedo, J. Nuno Fidalgo
    International Conference on the European Energy Market Eem, 2025
    Ensuring reliable and high-quality electricity service is critical for consumers and Distribution System Operators (DSO). The DSO's Plan for Development and Investment in the Distribution Network (PDIDN) plays a pivotal role in enhancing network reliability and resilience while balancing technical and financial aspects. This study proposes a novel probabilistic approach for quality-of-service (QoS) estimation in distribution systems, addressing the limitations of traditional deterministic methods. Leveraging Bayesian regression, specifically the Spike and Slab technique, the model incorporates prior knowledge to improve the prediction of key QoS indicators such as SAIDI, SAIFI, and TIEPI. Using historical network data, the model demonstrates superior predictive accuracy and robustness, offering realistic confidence intervals for strategic planning. This method enables informed investments, enhances regulatory compliance, and supports renewable integration. The findings underline the potential of probabilistic modeling in advancing QoS forecasting, encouraging its application in other areas of electric network management.
  • Photovoltaic Projects for Multidimensional Poverty Alleviation: Bibliometric Analysis and State of the Art
    Leonarda F. C. Castro, Paulo C. M. Carvalho, João P. T. Saraiva, José Nuno Fidalgo
    International Journal of Energy Economics and Policy, 2024
    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.
  • Decision Aid Tool to Mitigate Quality of Service Asymmetries in Distribution Networks
    Pedro Macedo, José Nuno Fidalgo
    International Conference on the European Energy Market Eem, 2024
    This article presents a methodology to estimate the evolution of QoS indices, based on investments and maintenance costs carried out in the DN. The indices were estimated at various disaggregated levels, including the global index, 3 different QoS zones (urban, semi-urban and rural) and 278 municipalities, thereby facilitating the mitigation of QoS asymmetries by allocating investments and maintenance actions to specific regions. To achieve this objective, an optimization problem was formulated to allocate investments and maintenance costs to municipalities with higher improvement benefit-cost ratios, potentially exhibiting lower levels of QoS. This methodology was adopted by the Portuguese DSO to establish the future investments plan from 2023 to 2027. The results demonstrate estimations of good performance, considering the stochastic nature of the phenomena affecting QoS (e.g. atmospheric conditions), which are included in this study, thus developing confidence levels for the global indices.
  • Data-driven Approach for High Loss Detection in LV Networks
    José Pedro Paulos, Pedro Macedo, Ricardo Bessa, J. Nuno Fidalgo, José Oliveira
    IEEE Pes Innovative Smart Grid Technologies Europe Isgt Europe 2024, 2024
    This article proposes a methodology for high loss detection in LV network, based on a very small set of commonly available data/metadata from networks connected to an MV/LV substation. The approach is based on a combination of predictors from several distinct categories, including network data, metadata, and measured smart meter data. Several independent groups of unranked real networks were simulated, and it was possible to find the top ten networks with the highest level of losses with a very satisfactory success rate (76% to 98%), depending on selected groupings folds. Due to the impracticability of analyzing all LV networks, the identification of the highest loss ones is essential for the definition of loss reduction planning since, with this list filtering, it is possible to determine with a good degree of certainty which networks require maintenance or upgrade.
  • Easing Predictors Selection in Electricity Price Forecasting with Deep Learning Techniques
    Ana Rita Silva, José Nuno Fidalgo, José Ricardo Andrade
    International Conference on the European Energy Market Eem, 2023
    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.
  • Estimation of Planning Investments with Scarce Data - Comparing LASSO, Bayesian and CMLR
    José Nuno Fidalgo, Pedro M. Macedo, Hugo F. R. Rocha
    International Conference on the European Energy Market Eem, 2023
    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.
  • Decision support system for long-term reinforcement planning of distribution networks
    J. Nuno Fidalgo, F. Azevedo
    Electric Power Systems Research, 2022
  • Identification of Typical and Anomalous Patterns in Electricity Consumption
    José Nuno Fidalgo, Pedro Macedo
    Applied Sciences Switzerland, 2022
    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.
  • The Value of Investments in Network Efficiency in Systems with a Large Integration of Distributed Renewable Generation
    Jose Nuno Fidalgo, Jose Pedro Paulos, Pedro Macedo
    International Conference on the European Energy Market Eem, 2022
  • Comparison Among National Energy Community Policies in Brazil, Germany, Portugal, and Spain
    Leonarda F. C. Castro, Paulo C. M. Carvalho, J. N. Fidalgo, J. T. Saraiva
    International Conference on the European Energy Market Eem, 2022
  • Non-Intrusive Load Monitoring for Household Disaggregated Energy Sensing
    Jose Pedro Paulos, Jose Nuno Fidalgo, Joao Gama
    2021 IEEE Madrid Powertech Powertech 2021 Conference Proceedings, 2021
  • Estimation of the Global Amount of Mandatory Investments for Distribution Network Expansion Planning
    Pedro Miguel Macedo, Jose Nuno Fidalgo, Joao Tome Saraiva
    2021 IEEE Madrid Powertech Powertech 2021 Conference Proceedings, 2021
  • Detection and Mitigation of Extreme Losses in Distribution Networks
    Jose Pedro Paulos, Jose Nuno Fidalgo, J. T. Saraiva, Nuno Barbosa
    2021 IEEE Madrid Powertech Powertech 2021 Conference Proceedings, 2021
  • Predicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models
    Matheus Paula, Colnago Marilaine, Fidalgo Jose Nuno, Casaca Wallace
    IEEE Latin America Transactions, 2020
  • Cost-benefit Analysis on a New Access Tariff: Case Study on the Portuguese System
    P. Vilaca, J. T. Saraiva, J. N. Fidalgo
    International Conference on the European Energy Market Eem, 2020
  • Assessing the Impact of Investments in Distribution Planning
    Pedro Macedo, Jose Nuno Fidalgo, Joao Tome Saraiva
    International Conference on the European Energy Market Eem, 2020
  • Classification of Buildings Energetic Performance Using Artificial Immune Algorithms
    Jose Pedro Alves, Jose Nuno Fidalgo
    Sest 2019 2nd International Conference on Smart Energy Systems and Technologies, 2019
  • Impact of Climate Changes on the Portuguese Energy Generation Mix
    J. Nuno Fidalgo, Debora de Sao Jose, Carlos Silva
    International Conference on the European Energy Market Eem, 2019
  • Impact of load unbalance on low voltage network losses
    J. Nuno Fidalgo, Carlos Moreira, Rafael Cavalheiro
    2019 IEEE Milan Powertech Powertech 2019, 2019
  • Load and electricity prices forecasting using generalized regression neural networks
    Jose Pedro Paulos, Jose Nuno Fidalgo
    2018 International Conference on Smart Energy Systems and Technologies Sest 2018 Proceedings, 2018
  • Improving electricity price forecasting trough data segmentation based on artificial immune systems
    J. Nuno Fidalgo, Eduardo F. N. R. Da Rocha
    International Conference on the European Energy Market Eem, 2018
  • The use of smart grids to increase the resilience of Brazilian power sector to climate change effects
    Débora de São José, J. Nuno Fidalgo
    IFIP Advances in Information and Communication Technology, 2018
  • Transparency versus efficiency in the MIBEL market
    Jose Nuno Fidalgo, Paulo Adelino P. L. da Rocha
    International Conference on the European Energy Market Eem, 2017