2013
Authors
Pereira, Ivo F.; Praça, Isabel; Pinto, Tiago; Sousa, Tiago; Freitas, Ana; Vale, Zita;
Publication
First ELECON Workshop – Towards Efficient European and Brazilian Electricity Markets
Abstract
The study of Electricity Markets operation has been gaining an increasing importance in the last years,
as result of the new challenges that the restructuring produced. Currently, lots of information
concerning Electricity Markets is available, as market operators provide, after a period of
confidentiality, data regarding market proposals and transactions. These data can be used as source of
knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets
behaviour. The development of tools able to extract, transform, store and dynamically update data, is of
great importance to go a step further into the comprehension of Electricity Markets and the behaviour
of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and
storing data from market operators’ websites, assuring actualization and reliability of stored data.
2014
Authors
Marques, Luís; Pinto, Tiago; Sousa, Tiago; Praça, Isabel; Vale, Zita; Abreu, Samuel L.;
Publication
Second ELECON Workshop – Consumer control in Smart Grids
Abstract
This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.
2021
Authors
Santos, G; Gomes, L; Pinto, T; Vale, Z; Faria, P;
Publication
Abstract
2021
Authors
Gomes, L; Vale, Z; Pinto, T;
Publication
Abstract
2022
Authors
Gomes, L; Pinto, T; Vale, Z;
Publication
Abstract
2023
Authors
Veiga, B; Santos, G; Pinto, T; Faia, R; Ramos, C; Vale, Z;
Publication
Abstract
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