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About

Ricardo Bessa was born in 1983 in Viseu, received his Licenciado (five-year) degree from the Faculty of Engineering of the University of Porto, Portugal (FEUP) in 2006 in Electrical and Computer Engineering. In 2008, he received the M.Sc. degree in Data Analysis and Decision Support Systems on the Faculty of Economics of the University of Porto (FEP). He obtained his Ph.D. degree in the Doctoral Program in Sustainable Energy Systems (MIT Portugal) at FEUP in 2013. Currently, he is a Senior Researcher and Area Manager at INESC TEC in its Center for Power and Energy Systems. 

His research interests include renewable energy forecasting, electric vehicles, data mining and decision-making under risk. He worked in several international projects such as the European Projects FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 EvolvDSO, Horizon 2020 UPGRID, Horizon 2020 InteGrid and an international collaboration with Argonne National Laboratory for the U.S. Department of Energy. At the national level he participated in the development of renewable energy forecasting systems and consultant services about energy storage.

He is co-authors of more than 32 journal papers and 61 conference papers.

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Details

Details

  • Name

    Ricardo Jorge Bessa
  • Cluster

    Power and Energy
  • Role

    Centre Coordinator
  • Since

    01st February 2006
059
Publications

2021

Towards Data Markets in Renewable Energy Forecasting

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

Publication
IEEE Transactions on Sustainable Energy

Abstract

2021

A critical overview of privacy-preserving approaches for collaborative forecasting

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
IEEE Transactions on Sustainable Energy

Abstract

Supervised
thesis

2020

Energy Markets Simulation and Forecasting with Emergent Business Models

Author
Renato Fernandes

Institution
UP-FEP

2020

Renewable Energy Forecasting – Extreme Quantiles, Data Privacy and Monetization

Author
Carla Sofia da Silva Gonçalves

Institution
UP-FCUP

2020

State Estimation for Evolving Power Systems Paradigms

Author
Gil da Silva Sampaio

Institution
UP-FEUP

2020

Comparision between traditional network reinforcement and the use of DER flexibility

Author
Tiago José Pires de Almeida Torres

Institution
UP-FEUP

2020

Improving solar power forecasting through advanced feature engineering

Author
Rui Manuel Gonçalves do Couto

Institution
UP-FEUP