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About

About

I was born in Porto in 1992. I have an undergraduate's degree in mathematics and a master's degree in applied mathematics. I am currently a PhD candidate in applied mathematics (FCUP). I work in machine learning, more concretely in probabilistic forecast of renewable energies.

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Details

001
Publications

2020

Towards Data Markets in Renewable Energy Forecasting

Authors
Goncalves, C; Pinson, P; Bessa, RJ;

Publication
IEEE Transactions on Sustainable Energy

Abstract

2019

A methodology to evaluate the uncertainties used to perform security assessment for branch overloads

Authors
Vasconcelos, MH; Goncalves, C; Meirinhos, J; Omont, N; Pitto, A; Ceresa, G;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems. © 2019 Elsevier Ltd

2019

Explanatory and causal analysis of the MIBEL electricity market spot price

Authors
Goncalves, C; Ribeiro, M; Viana, J; Fernandes, R; Villar, J; Bessa, R; Correia, G; Sousa, J; Mendes, V; Nunes, AC;

Publication
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
This paper analyzes the electricity prices of the MIBEL electricity spot market with respect to a set of possible explanatory variables. Understanding the main drivers of the electricity price is a key aspect in understanding price formation and in developing forecasting models, which are essential for the selling and buying strategies of market agents. For this analysis, different techniques have been applied in this work, including standard and lasso regression models, causal analysis based on Bayesian networks and classification trees. Results from the different approaches are coherent and show strong dependency of the electricity prices with the Portuguese imported coal for lower non-dispatchable net demands, which has been progressively replaced by gas for larger non-dispatchable net demands. Hydro reservoirs and hydro production are also main explanatory variables of the electricity price for all non-dispatchable net demand levels. © 2019 IEEE.

2019

Evaluation of the uncertainties used to perform flow security assessment: A real case study

Authors
Vasconcelos, MH; Goncalves, C; Meirinhos, J; Omont, N; Pitto, A; Ceresa, G;

Publication
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
In this paper, a validation framework is proposed to evaluate the quality of uncertainty forecasts, when used to perform branch flow security assessment. The consistency between probabilistic forecasts and observations and the sharpness of the uncertainty forecasts is verified with advanced metrics widely used in weather prediction. The evaluation is completed by assessing the added value of exploiting uncertainty forecasts over the TSO current practices of using deterministic forecasts. For electric power industry, this proposed validation framework provides a way to compare the performance among alternative uncertainty models, when used to perform security assessment in power systems. The quality of the proposed metrics is illustrated and validated on historical data of the French transmission system. © 2019 IEEE.

2018

Data economy for prosumers in a smart grid ecosystem

Authors
Bessa, RJ; Rua, D; Abreu, C; Machado, P; Andrade, JR; Pinto, R; Goncalves, C; Reis, M;

Publication
e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems

Abstract
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources. © 2018 Copyright held by the owner/author(s).