Energy Disaggregation and Novel Eco Feedback Approaches

The work in this topic addresses the practical implications of deploying and long-term testing Non-Intrusive Load Monitoring (NILM) and novel eco-feedback approaches in real-world scenarios.
Overall, the research contributions in this topic are centered around: i) the design, implementation and long-term deployment of energy disaggregation and eco-feedback technologies in real world scenarios [1], ii) the development of tools and datasets for energy disaggregation and eco-feedback research [2], and iii) experimental comparison of performance metrics for event detection and event classification algorithms using real-world datasets [3].
Ultimately, so far, this work has led to the successful application of 3 Research & Development grants: i) the Smart Solar Energy Monitoring and Management project (SmartSolar), where our hardware-software platforms were used to simultaneously monitor energy consumption and micro-production from solar PV installations, ii) the H2020 SME instrument phase I, with (a M-ITI spin-off for sustainable energy research and development) aimed at developing and exploring business feasibility of the EnerSpectrum energy monitoring and eco-feedback platform for domestic environments, and iii) the H2020 Smart Island Energy Systems project (SMILE) aimed at demonstration the real-word applicability of Demand Side Management and Smart-charging of Electric Vehicles in Madeira Island.



References to the Research

  • Pereira, L., Quintal, F.., Barreto, M. and Nunes, N.J. (2013) “Understanding the Limitations of Eco-feedback: A One Year Long-term study” In Proceedings of the ACM / IEEE Conference on Human Factors in Computing & Informatics, SouthCHI ’13
  • Pereira, L., Quintal, F., Gonçalves, R. and Nunes, N.J. (2014) “SustData: A Public Dataset for ICT4S Electric Energy Research,” In Proceedings of the International Conference of ICT for Sustainability 2014, ICT4S ’14
  • Pereira, L., and Nunes, N.J. (2017) “A Comparison of Performance Metrics for Event Classification in Non-Intrusive Load Monitoring”, In Proceedings of the IEEE International Conference on Smart Grid Communications, SmartGridComm ’17


Sources to Corroborate the Impact