2019
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
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
Authors
Viana, J; Bessa, RJ; Sousa, J;
Publication
2019 IEEE MILAN POWERTECH
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.
2021
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
2021
Authors
Dorokhova, M; Ribeiro, F; Barbosa, A; Viana, J; Soares, F; Wyrsch, N;
Publication
ENERGIES
Abstract
The energy efficiency requirements of most energy-consuming sectors have increased recently in response to climate change. For buildings, this means targeting both facility managers and building users with the aim of identifying potential energy savings and encouraging more energy-responsible behaviors. The Information and Communication Technology (ICT) platform developed in Horizon 2020 FEEdBACk project intends to fulfill these goals by enabling the optimization of energy consumption, generation, and storage and control of flexible devices without compromising comfort levels and indoor air quality parameters. This work aims to demonstrate the real-world implementation and functionality of the ICT platform composed of Load Disaggregation, Net Load Forecast, Occupancy Forecast, Automation Manager, and Behavior Predictor applications. Particularly, the results obtained by individual applications during the test phase are presented alongside the specific metrics used to evaluate their performance.
2021
Authors
Menci, SP; Bessa, RJ; Herndler, B; Korner, C; Rao, BV; Leimgruber, F; Madureira, AA; Rua, D; Coelho, F; Silva, JV; Andrade, JR; Sampaio, G; Teixeira, H; Simoes, M; Viana, J; Oliveira, L; Castro, D; Krisper, U; Andre, R;
Publication
ENERGIES
Abstract
2021
Authors
Goncalves C.; Ribeiro M.; Viana J.; Fernandes R.; Villar J.; Bessa R.; Correia G.; Sousa J.; Mendes V.; Cristina Nunes A.;
Publication
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
Abstract
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