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Details

  • Name

    João Paulo Viana
  • Cluster

    Power and Energy
  • Role

    Research Assistant
  • Since

    16th January 2018
004
Publications

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.

2019

Explanatory and causal analysis of the MIBEL electricity market spot price

Authors
Goncalves, C; Ribeiro, M; Viana, J; Fernandes, R; Villar, J; Bessa, R; Correia, G; Sousa, J; Mendes, V; Nunes, AC;

Publication
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
This paper analyzes the electricity prices of the MIBEL electricity spot market with respect to a set of possible explanatory variables. Understanding the main drivers of the electricity price is a key aspect in understanding price formation and in developing forecasting models, which are essential for the selling and buying strategies of market agents. For this analysis, different techniques have been applied in this work, including standard and lasso regression models, causal analysis based on Bayesian networks and classification trees. Results from the different approaches are coherent and show strong dependency of the electricity prices with the Portuguese imported coal for lower non-dispatchable net demands, which has been progressively replaced by gas for larger non-dispatchable net demands. Hydro reservoirs and hydro production are also main explanatory variables of the electricity price for all non-dispatchable net demand levels. © 2019 IEEE.

2019

Load forecasting benchmark for smart meter data

Authors
Viana, J; Bessa, RJ; Sousa, J;

Publication
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
Actual integration of high-tech devices brings opportunities for better monitoring, management and control of low voltage networks. In this new paradigm, efficient tools should cope with the great amount of dispersed and considerably distinct data to support smarter decisions in almost real time. Besides the use of tools to enable an optimal network reconfiguration and integration of dispersed and renewable generation, the impact evaluation of integrating storage systems, accurate load forecasting methods must be found even when applied to individual consumers (characterized by the high presence of noise in time series). As this effort becomes providential in the smart grids context, this article compares three different approaches: one based on Kernel Density Estimation, an alternative based on Artificial Neural Networks and a method using Support Vector Machines. The first two methods revealed unequivocal benefits when compared to a Naive method consisting of a simple reproduction of the last available day. © 2019 IEEE.