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About

About

Pedro Macedo holds a M.Sc. degree in Electrical and Computer Engineering with specialisation in energy systems, from the Faculty of Engineering of the University of Porto (FEUP), since 2014.

Since then he develops his activity as a R&D in the area of System Planning and Reliability at the Centre for Power and Energy Systems (CPES), where he works in research projects in partnership with industry. He has been focusing his activity in the area of data mining and in the forecast models development applied to energy electrical systems.

Interest
Topics
Details

Details

  • Name

    Pedro Miguel Macedo
  • Cluster

    Power and Energy
  • Role

    Researcher
  • Since

    06th April 2015
009
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.

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

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.

2021

Estimation of the Global Amount of Mandatory Investments for Distribution Network Expansion Planning

Authors
Macedo, PM; Fidalgo, JN; Saraiva, JT;

Publication
2021 IEEE MADRID POWERTECH

Abstract
The financial planning of distribution systems usually includes the prediction of annual mandatory investments, concerning the resources that the DSO is compelled to allocate as a result of new network connections, required by new consumers or new energy producers. This paper presents a methodology to estimate the mandatory investments that the DSO should do in the distribution network. These estimations are based on historical data, load growth expectations and various socioeconomic indices. However, the available database contains very few annual investment examples (one aggregated value per year since 2002) compared to the large number of variables (potential inputs), which is a factor of regression overfitting. Thus, the applicable regression techniques are restrained to simple but efficient models. This paper describes a new methodology to identify the most suitable estimation models. The implemented application automatically builds, selects, and tests estimation models resulting from combinations of input variables. The final forecast is provided by a committee of models. Results obtained so far confirm the feasibility of the adopted methodology.

2020

Assessing the Impact of Investments in Distribution Planning

Authors
MacEdo, P; Fidalgo, JN; Tome Saraiva, J;

Publication
International Conference on the European Energy Market, EEM

Abstract
The expansion and development of the electricity distribution grid is a complex multicriteria decision problem. The planning definition should take into consideration the investment benefits on the security of supply, quality of service, losses, as well as in other network features. Given the variety of assets and their context-dependent effects, estimating their global impact is very challenging. An additional difficulty is the combination of different types of benefits into a simple and clear portrayal of the planning alternatives. This paper proposes a methodology to estimate the benefits of distribution investments, in terms of five features: security of supply, quality of service, network losses, operational efficiency and new services. The approach is based on the adoption of objective and measurable indicators for each feature. The approach was tested with real data of Portuguese distribution grids and the results support the adopted approach and are being used as a decision-aid tool for grid planning. © 2020 IEEE.

Supervised
thesis

2022

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

Author
Hugo Francisco Rocha Costa

Institution
UP-FEUP

2021

Previsão de investimentos com base em informação esparsa

Author
João Pedro Espírito Santo Almeida

Institution
UP-FEUP

2020

Previsão de investimentos com base em informação esparsa

Author
João Pedro Espírito Santo Almeida

Institution
UP-FEUP