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Sobre

Ricardo Bessa nasceu in 1983 em Viseu. Completou a licenciatura em Engenharia Eletrotécnica pela Faculdade de Engenharia da Universidade do Porto (FEUP) em 2006, o mestrado em Análise de Dados e Sistemas de Apoio à Decisão pela Faculdade de Economia da Universidade do Porto (FEP) em 2008 e o Doutoramento em Sistemas Sustentáveis de Energia pela FEUP em 2013.

É Investigador Sénior no INESC TEC desde 2006 no Centro de Sistemas de Energia. Foi investigador em diversos projetos relacionados com previsão eólica e sua integração na gestão do sistema elétrico de energia. Tem participado ativamente em projetos relacionados com redes elétricas inteligentes, nomeadamente os Projetos Europeus FP7 SusTAINABLE e evolvDSO e os projetos Horizonte 2020 UPGRID e InteGrid (onde é coordenador técnico).

Os seus interesses de I&D são energias renováveis, veículos elétricos, extração de conhecimento de dados e apoio à decisão.  

Tem publicado 32 artigos em revistas internacionais e 61 artigos em conferências internacionais.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Jorge Bessa
  • Cluster

    Energia
  • Cargo

    Coordenador de Centro
  • Desde

    01 fevereiro 2006
061
Publicações

2021

Towards Data Markets in Renewable Energy Forecasting

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

Publicação
IEEE Transactions on Sustainable Energy

Abstract

2021

A critical overview of privacy-preserving approaches for collaborative forecasting

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

Publicação
International Journal of Forecasting

Abstract
Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations. © 2020 International Institute of Forecasters

2021

A deep learning method for forecasting residual market curves

Autores
Coronati, A; Andrade, JR; Bessa, RJ;

Publicação
Electric Power Systems Research

Abstract
Forecasts of residual demand curves (RDCs) are valuable information for price-maker market agents since it enables an assessment of their bidding strategy in the market-clearing price. This paper describes the application of deep learning techniques, namely long short-term memory (LSTM) network that combines past RDCs and exogenous variables (e.g., renewable energy forecasts). The main contribution is to build up on the idea of transforming the temporal sequence of RDCs into a sequence of images, avoiding any feature reduction and exploiting the capability of LSTM in handling image data. The proposed method was tested with data from the Iberian day-ahead electricity market and outperformed machine learning models with an improvement of above 35% in both root mean square error and Frèchet distance. © 2020

2021

Forecasting conditional extreme quantiles for wind energy

Autores
Goncalves, C; Cavalcante, L; Brito, M; Bessa, RJ; Gama, J;

Publicação
Electric Power Systems Research

Abstract
Probabilistic forecasting of distribution tails (i.e., quantiles below 0.05 and above 0.95) is challenging for non-parametric approaches since data for extreme events are scarce. A poor forecast of extreme quantiles can have a high impact in various power system decision-aid problems. An alternative approach more robust to data sparsity is extreme value theory (EVT), which uses parametric functions for modelling distribution's tails. In this work, we apply conditional EVT estimators to historical data by directly combining gradient boosting trees with a truncated generalized Pareto distribution. The parametric function parameters are conditioned by covariates such as wind speed or direction from a numerical weather predictions grid. The results for a wind power plant located in Galicia, Spain, show that the proposed method outperforms state-of-the-art methods in terms of quantile score. © 2020 Elsevier B.V.

2021

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

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

Publicação
IEEE Transactions on Sustainable Energy

Abstract

Teses
supervisionadas

2020

Improving solar power forecasting through advanced feature engineering

Autor
Rui Manuel Gonçalves do Couto

Instituição
UP-FEUP

2020

Residential Consumer Behavioural Analysis on the participation in Demand Response Strategies including distributed generation and electric vehicles

Autor
Kamalanathan Ganesan

Instituição
UP-FEUP

2020

Energy Markets Simulation and Forecasting with Emergent Business Models

Autor
Renato Fernandes

Instituição
UP-FEP

2020

Renewable Energy Forecasting – Extreme Quantiles, Data Privacy and Monetization

Autor
Carla Sofia da Silva Gonçalves

Instituição
UP-FCUP

2020

State Estimation for Evolving Power Systems Paradigms

Autor
Gil da Silva Sampaio

Instituição
UP-FEUP