Lucas Pereira
Biography
Lucas Pereira received his PhD in Computer Science from the University of Madeira, Portugal, in 2016. Since then, he has been at ITI/LARSyS, leading the Further Energy and Environment Research Laboratory (FEELab). Since 2019, he has been an Assistant Researcher at Instituto Superior Técnico, University of Lisbon. Lucas’s research applies data science, machine learning, and human-computer interaction techniques toward bridging the gap between laboratory and real-world applicability of ICT for sustainable development goals (SDGs). His current research focuses on future energy systems and sustainable built environments, and it typically involves the real-world deployment and evaluation of monitoring technologies and software systems.
Related Projects
News
Publications
2021
Watt's up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective Journal Article
In: Energies, vol. 14, no. 3, pp. 719, 2021.
Special Issue: "Energy Data Analytics for Smart Meter Data" Journal Article
In: Energies, vol. 14, no. 17, pp. 5376, 2021, ISSN: 1996-1073.
2020
PB-NILM: Pinball Guided Deep Non-Intrusive Load Monitoring Journal Article
In: IEEE Access, vol. 8, pp. 48386–48398, 2020.
A global monitoring system for electricity consumption and production of household roof-top PV systems in Madeira Journal Article
In: Neural Comput. Appl., vol. 32, no. 20, pp. 15835–15844, 2020.
Understanding the Challenges behind Electric Vehicle Usage by Drivers - a Case Study in the Madeira Autonomous Region Proceedings Article
In: Proceedings of the 7th International Conference on ICT for Sustainability, pp. 88–97, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-7595-5.
Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks Journal Article
In: Energies, vol. 13, no. 13, pp. 3374, 2020.
Leveraging Machine Learning for Sustainable and Self-sufficient Energy Communities Proceedings Article
In: Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020, Climate Change AI, Online, 2020.
Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network Journal Article
In: Energies, vol. 13, no. 16, pp. 4154, 2020.
UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM Proceedings Article
In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 84–88, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-8191-8.
On the Relationship between Seasons of the Year and Disaggregation Performance Proceedings Article
In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 70–74, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-8191-8.