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About

Ricardo was born in 1992 in Sta. Maria da Feira, Portugal, received the M.S. degree from the Faculty of Engineering of the University of Porto, Portugal (FEUP) in 2016 in Electrical and Computer Engineering.

His main interests are data analysis/visualization and machine learning concepts applied to renewable energy and electricity prices forecasting.

Interest
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Details

Details

  • Name

    José Ricardo Andrade
  • Cluster

    Power and Energy
  • Role

    Researcher
  • Since

    01st July 2016
008
Publications

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

FEEdBACk: An ICT-Based Platform to Increase Energy Efficiency through Buildings’ Consumer Engagement

Authors
Soares, F; Madureira, A; Pages, A; Barbosa, A; Coelho, A; Cassola, F; Ribeiro, F; Viana, J; Andrade, J; Dorokhova, M; Morais, N; Wyrsch, N; Sorensen, T;

Publication
Energies

Abstract
Energy efficiency in buildings can be enhanced by several actions: encouraging users to comprehend and then adopt more energy-efficient behaviors; aiding building managers in maximizing energy savings; and using automation to optimize energy consumption, generation, and storage of controllable and flexible devices without compromising comfort levels and indoor air-quality parameters. This paper proposes an integrated Information and communications technology (ICT) based platform addressing all these factors. The gamification platform is embedded in the ICT platform along with an interactive energy management system, which aids interested stakeholders in optimizing “when and at which rate” energy should be buffered and consumed, with several advantages, such as reducing peak load, maximizing local renewable energy consumption, and delivering more efficient use of the resources available in individual buildings or blocks of buildings. This system also interacts with an automation manager and a users’ behavior predictor application. The work was developed in the Horizon 2020 FEEdBACk (Fostering Energy Efficiency and BehAvioral Change through ICT) project.

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).

2017

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Authors
Andrade, JR; Filipe, J; Reis, M; Bessa, RJ;

Publication
SUSTAINABILITY

Abstract
Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 Euro/MWh for day-ahead market and a maximum value of 2.53 Euro/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

2017

Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions

Authors
Andrade, JR; Bessa, RJ;

Publication
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.