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About

About

Carla Gonçalves is a Postdoctoral Researcher at the Centre for Power and Energy Systems in INESC TEC. In 2021, she obtained a Ph.D. in Applied Mathematics from the Faculty of Sciences of the University of Porto (FCUP). She received the M.Sc. in Applied Mathematics from FCUP, in 2015. From 2015 to 2019, she was involved in a wide range of energy forecast consulting collaborations between INESC TEC and the industry: REN (Portugal), EDP Renewables (Spain), RTE (France), and EDP Gestão da Produção de Energia (Portugal). Until 2022, she was associated with the H2020 Smart4RES project, and since 2023, she is taking part in the European ENERSHARE and GREEN.DAT.AI projects. Her research has been focused on probabilistic and collaborative forecasting methods, with a special emphasis on renewable energies, data privacy, and monetization. During her scientific career, she has co-authored 13 scientific papers (6 Q1 journals with impact factors between 3.818 and 8.310, and 7 international conference proceedings) and has submitted a patent (currently pending). She also served as an Invited Auxiliary Professor at the University of Porto for two semesters.

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Details

Details

  • Name

    Carla Silva Gonçalves
  • Role

    Assistant Researcher
  • Since

    12th October 2015
005
Publications

2022

Conditional parametric model for sensitivity factors in LV grids: A privacy-preserving approach

Authors
Sampaio, G; Bessa, RJ; Goncalves, C; Gouveia, C;

Publication
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
The deployment of smart metering technologies in the low voltage (LV) grid created conditions for the application of data-driven monitoring and control functions. However, data privacy regulation and consumers' aversion to data sharing may compromise data exchange between utility and customers. This work presents a data-driven method, based on smart meter data, to estimate linear sensitivity factors for three-phase unbalanced LV grids, which combines a privacy-preserving protocol and varying coefficients linear regression. The proposed method enables centralized and peer-to-peer learning of the sensitivity factors. Potential applications for the sensitivity factors are demonstrated by solving voltage violations or computing operating envelopes in a LV grid without resorting to its network topology or electrical parameters.

2022

A Blockchain-based Data Market for Renewable Energy Forecasts

Authors
Coelho, F; Silva, F; Goncalves, C; Bessa, R; Alonso, A;

Publication
2022 FOURTH INTERNATIONAL CONFERENCE ON BLOCKCHAIN COMPUTING AND APPLICATIONS (BCCA)

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

2021

Towards Data Markets in Renewable Energy Forecasting

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

Publication
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve renewable energy sources (RES) forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.

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.

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.