Publicações

Publicações em Congressos Internacionais

Aria Jozi, Tiago Pinto, Isabel Praça, Francisco Silva, Brigida Teixeira, Zita Vale, “Energy Consumption Forecasting using Genetic fuzzy rule-based systems based on MOGUL Learning Methodology”, PowerTech 2017 – 12th IEEE PES PowerTech Conference, Manchester, UK, 18-22 June, 2017 – doi: https://ieeexplore.ieee.org/document/7981219/.
Shohreh Ahvar, Gabriel Santos, Nouredine Tamani, Bernard Istasse, Isabel Praça, Paul-Emmanuel Brun, Yacine Ghamri, and Noël Crespi, “Ontology-based Model for Trusted Critical Site Supervision in FUSE-IT”, ICIN 17- 20th ICIN conference Innovations in Cloud, Internet and Networks, Paris, France, 7 – 9 March, 2017.
Aria Jozi, Tiago Pinto, Isabel Praça, Francisco Silva, Brigida Teixeira e Zita Vale, “Energy Consumption Forecasting based on Hybrid Neural Fuzzy Inference System”, CIASG 2016 – IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) at the IEEE SSCI 2016 (IEEE Symposium Series on Computational Intelligence), Athens, Greece, 6 – 9 December, 2016 – doi: https://ieeexplore.ieee.org/document/7849859/.
Hélia Pouyllau; Bernard Istasse; Shohreh Ahvar; Noël Crespi; Isabel Praça; Sandra Garcia Rodriguez; Erhan Mengusoglu, “Enhancing Critical Site Supervision with Cross-Domain Key Performance Indicators”, Global Information Infrastructure and Networking Symposium (GIIS 2016), Porto – Oct 19-21, 2016.
Aria Jozi; Tiago Pinto; Isabel Praça; Francisco Silva; Brigida Teixeira; Zita Vale, “Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting”, Global Information Infrastructure and Networking Symposium (GIIS 2016), Porto – Oct 19-21, 2016 – doi: https://ieeexplore.ieee.org/document/7814944/.
Zita Vale, “Interoperability of real-time agent-based intelligent systems for demand response in Smart grids”, Keynote, Global Information Infrastructure and Networking Symposium (GIIS 2016), Porto – Oct 19-21, 2016
Adrien Bécue, “Intelligent building: at the convergence between energy, facility, ICT and security”, Keynote, Global Information Infrastructure and Networking Symposium (GIIS 2016), Porto – Oct 19-21, 2016.
Aria Jozi, Tiago Pinto, Isabel Praça, Sérgio Ramos, Zita Vale, Benedicte Goujon, Petrisor Teodora, “Energy Consumption Forecasting using Neuro-Fuzzy Inference Systems: Thales TRT building case study”, SSCI 2017 – The 2017 IEEE Symposium on Computational Intelligence, Honolulu, Hawaii, USA – 27 Nov-1 Dec, 2017 – doi: https://ieeexplore.ieee.org/document/8285200/.
João Soares, Fernando Lezama, Sérgio Ramos, Zita Vale and André Lopes, “A Residential Energy Management System with Offline Population-Based Optimization”, SSCI 2017 – The 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, Hawaii, USA – 27 Nov-1 Dec, 2017 – doi: https://ieeexplore.ieee.org/document/8285429/.
João Spínola, Pedro Faria, Zita Vale, “Energy Resource Scheduling with Multiple Iterations for the Validation of Demand Response Aggregation”, T&D 2018 – IEEE Transmission & Distribution Conference & Exposition Denver, USA – 17-19 April, 2018.
Mahsa Khorram, Pedro Faria, Zita Vale, “Optimization-Based Home Energy Management System Under Different Electricity Pricing Schemes”, INDIN 2018 – 16th International Conference on Industrial Informatics, Porto, Portugal, 18-20 July, 2018 – doi: https://doi.org/10.1109/INDIN.2018.8472101.

Publicações em Livro (Artigos)

Gil Pinheiro, Eugénia Vinagre, Isabel Praça, Zita Vale, Carlos Ramos, “Smart Grids Data Management: A Case for Cassandra”, Distributed Computing and Artificial Intelligence, 14th International Conference, pp. 87-95, Advances in Intelligent Systems and Computing, vol. 620, Springer International Publishing, 2017 – doi: https://link.springer.com/chapter/10.1007%2F978-3-319-62410-5_11.
Catarina Ribeiro, Tiago Pinto, Zita Vale, José Baptista, “Data Mining for Prosumers Aggregation considering the Self-Generation”, Distributed Computing and Artificial Intelligence, 14th International Conference, pp. 96-103, Advances in Intelligent Systems and Computing, vol. 620, Springer International Publishing, 2017 – doi: https://link.springer.com/chapter/10.1007%2F978-3-319-62410-5_12.
Filipe Fernandes, Luis Gomes, Hugo Morais, Marco Silva, Zita vale, Juan M. Corchado, “Dynamic Energy Management Method with Demand Response Interaction Applied in an Office Building”, Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection, Advances in Intelligent Systems and Computing, vol. 473, pp. 69-82, Springer International Publishing, 2016.
Mahsa Khorram, Pedro Faria, Omid Abrishambaf, Zita Vale, ” Demand Response Implementation in an Optimization Based SCADA Model Under Real-Time Pricing Schemes”, Advances on Demand Response and Renewable Energy Sources in Smart Grids (ADRESS), 2018 – doi: https://doi.org/10.1007/978-3-319-99608-0_3.

Newsletters

Mohamed Attia, Sidi Mohammed Senouci, Shohrer Avhar˫, Jen Rossey, Luc Lutin, Isabel Praça, “Security Issues and Solutions in Smart Grid: FUSE-IT European Project Use-case”, Ad Hoc and Sensor Networks Technical Committee Newsletter (AHSN TCN), IEEE Communications Society, Vol 1, nr 10, 2017.

Artigos em Revistas Internacionais

Tiago Pinto, Luis Marques, Tiago Sousa, Isabel Praça, Zita Vale, Samuel Abreu, “Data-Mining-based filtering to support Solar Forecasting Methodologies”, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, vol. 6, no. 3 September, 2017 – doi: http://revistas.usal.es/index.php/2255-2863/article/view/ADCAIJ20176385102.
Pedro Faria, João Spínola, Zita Vale, “Methods for Aggregation and Remuneration of Distributed Energy Resources”, Applied Sciences, vol. 8, no. 8 August 2018 – doi: http://www.mdpi.com/2076-3417/8/8/1283.
Mahsa Khorram, Omid Abrishambaf, Pedro Faria, Zita Vale, “Office Building Participation in Demand Response Programs Supported by Intelligent Lighting Management”, Energy Informatics, August 2018 – doi: https://doi.org/10.1186/s42162-018-0008-4.
Cátia Silva, Pedro Faria, Zita Vale, “Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response”, Energies, vol. 11, no. 7, April 2019 – doi: https://doi.org/10.3390/en12071248.