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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
066
Publications

2022

How do Humans decide under Wind Power Forecast Uncertainty - An IEA Wind Task 36 Probabilistic Forecast Games and Experiments initiative

Authors
Möhrlen C.; Giebel G.; Bessa R.J.; Fleischhut N.;

Publication
Journal of Physics: Conference Series

Abstract
The need to take into account and explicitly model forecast uncertainty is today at the heart of many scientific and applied enterprises. For instance, the ever-increasing accuracy of weather forecasts has been driven by the development of ensemble forecasts, where a large number of forecasts are generated either by generating forecasts from different models or by repeatedly perturbing the initial conditions of a single forecast model. Importantly, this approach provides robust estimates of forecast uncertainty, which supports human judgement and decision-making. Although weather forecasts and their uncertainty are also crucial for the weather-to-power conversion for RES forecasting in system operation, power trading and balancing, the industry has been reluctant to adopt ensemble methods and other new technologies that can help manage highly variable and uncertain power feed-ins, especially under extreme weather conditions. In order to support the energy industry in the adaptation of uncertainty forecasts into their business practices, the IEA Wind Task 36 has started an initiative in collaboration with the Max Planck Institute for Human Development and Hans-Ertel Center for Weather Research to investigate the existing barriers in the industry to the adoption of such forecasts into decision processes. In the first part of the initiative, a forecast game was designed as a demonstration of a typical decision-making task in the power industry. The game was introduced in an IEA Wind Task 36 workshop and thereafter released to the public. When closed, it had been played by 120 participants. We will discuss the results of our first experience with the experiment and introduce some new features of the second generation of experiments as a continuation of the initiative. We will also discuss specific questions that emerged when we started and after analysing the experiments. Lastly we will discuss the trends we found and how we will fit these into the overall objective of the initiative which is to provide training tools to demonstrate the use and benefit of uncertainty forecasts by simulating decision scenarios with feedback and allowing people to learn from experience, rather than reading articles, how to use such forecasts.

2022

Functional model of residential consumption elasticity under dynamic tariffs

Authors
Ganesan, K; Saraiva, JT; Bessa, RJ;

Publication
ENERGY AND BUILDINGS

Abstract
One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific, which poses additional barriers for the active engagement of consumers in these programs. The elasticity of consumers' demand behavior varies from individual to individual. The utility will benefit from knowing more accurately how changes in its prices will modify the consumption pattern of its clients. This work proposes a functional model for the consumption elasticity of the DR contracted consumers. The model aims to determine the load adjustment the DR consumers can provide to the retailers or utilities for different price levels. The proposed model uses a Bayesian probabilistic approach to identify the actual load adjustment an individual contracted client can provide for different price levels it can experience. The developed framework provides the retailers or utilities with a tool to obtain crucial information on how an individual consumer will respond to different price levels. This approach is able to quantify the likelihood with which the consumer reacts to a DR signal and identify the actual load adjustment an individual contracted DR client provides for different price levels they can experience. This information can be used to maximize the control and reliability of the services the retailer or utility can offer to the System Operators. (c) 2021 Published by Elsevier B.V.

2022

Guest Editorial for the Special Section on Advances in Renewable Energy Forecasting: Predictability, Business Models and Applications in the Power Industry

Authors
Bessa, RJ; Pinson, P; Kariniotakis, G; Srinivasan, D; Smith, C; Amjady, N; Zareipour, H;

Publication
IEEE Transactions on Sustainable Energy

Abstract

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

Supervised
thesis

2021

Forecasting high-dimensional electrical energy time series

Author
Carla Sofia da Silva Gonçalves

Institution
UP-FCUP

2021

Multivariate Forecasting of Renewable Energy Generation

Author
José Pedro dos Santos Leite Ferreira

Institution
UP-FEP

2021

Residential Consumer Behavioural Analysis on the Participation in Demand Response Strategies

Author
Kamalanathan Ganesan

Institution
UP-FEUP

2021

Energy Markets Simulation and Forecasting with Emergent Business Models

Author
Renato Fernandes

Institution
UP-FEP

2021

State Estimation for Evolving Power Systems Paradigms

Author
Gil da Silva Sampaio

Institution
UP-FEUP