Recently my work was published in conference like IFIP Interact, ICT4S, IFIP SustainIT, IEEE SmartGridComm, and ACM BuildSys. Below is an up-to-date listing of my publications. Check my Google Scholar and ORCID profiles for additional information.
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Recently my work was published in conference like IFIP Interact, ICT4S, IFIP SustainIT, IEEE SmartGridComm, and ACM BuildSys. Below is an up-to-date listing of my publications. Check my Google Scholar and ORCID profiles for additional information.
Abstract
This paper presents the initial results of ultrasonic waste monitoring during the operation of one industrial kitchen. Undifferentiated, paper and plastic waste bins were monitored, and a heuristic-based waste disposal event detector was developed and evaluated. The results show that it is possible to identify disposal events in the three waste bins. Furthermore, it is also possible to determine when paper and plastic are compressed to make additional room.
Poster
Abstract
Large amounts of electricity, water, and food are used every day in Industrial Kitchens (IK). Still, very little attention has been devoted by the research community to this potential source of resource over-consumption. This abstract paper builds on the deployment of sensing technology in three IKs to present the main challenges and potential research directions towards more sustainable IKs.
Presentation Slides
Abstract
Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems.
Their scope and data resolution is, however, often limited to the means required to fulfill the experimenters’ objectives and moreover governed by personal experience, budgetary and time constraints, and the availability of equipment.
As a result, numerous differences between data sets can be observed, e.g., regarding their sampling rates, the number of sensors deployed, their amplitude resolutions, storage formats, or the availability and extent of ground-truth annotations.
This heterogeneity poses a significant problem for researchers intending to comparatively use data sets because of the required data conversion, re-sampling, and adaptation steps.
In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems.
We address this limitation by dissecting the collection methodologies used in existing data sets.
By offering recommendations for data collection, data storage, and data provision, we intend to foster the creation of data sets with increased usability and comparability, and thus a greater benefit to the community.
Presentation Slides
Abstract
This paper presents a Techno-Economic assessment of the value proposition of introducing battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against two local micro-producers using one year of energy consumption and solar PV production measurements. The assessments considered three different pairs of battery capacity/inverter size, and the outputs analyzed considering self-consumption, self-sufficiency, and energy costs. The results show that despite the increase in self-consumption and self-sufficiency, the value proposition of battery energy storage is still considerably low even considering a massive decrease in the costs of storage. Furthermore, the results also suggest that given the small size of the solar PV installations, inverters with half the size of the installed PV capacity represent the best value for money.
Poster
Abstract:
This paper presents the results of an empirical exploration of 10 cursor movement metrics designed to measure respondent hesitation in online surveys. As a use case, this work considers an online survey aimed at exploring how people gauge the electricity consumption of domestic appliances. The cursor metrics were derived computationally from the mouse trajectories when rating the consumption of each appliance and analyzed using Multidimensional Scaling, Jenks Natural Breaks, and the Jaccard Similarity Index techniques. The results show that despite the fact that the metrics measure different aspects of the mouse trajectories, there is an agreement with respect to the appliances that generated higher levels of hesitation. The paper concludes with an outline of future work that should be carried out in order to further understand how cursor trajectories can be used to measure respondent hesitation.
Poster:
Reproducible Research:
Data and scripts will be added soon.
Abstract
Current approaches for collecting and labeling Non- Intrusive Load Monitoring (NILM) datasets still rely heavily on a lengthy and error-prone manual inspection of the whole dataset. Consequently, it is still difficult to find fully labeled datasets that could help furthering, even more, the research in this field. In an attempt to overcome this situation, we propose a hardware and software platform to collect and label NILM sensor data in a semi-automatic labeling fashion. Our platform combines aggregate and plug-level smart-meters to measure consumption data, software algorithms to automatically detect changes in the different monitored loads and a graphical user interface where the end-user can supervise the labeling process. In this paper, we describe the different components that comprise our platform. We also present the results of one live deployment that was performed to test the feasibility of our approach. The results of the deployment show that our system was capable of explaining about 82% of the aggregate load, and automatically detect 94% of the power transitions in the plug-level loads.
Resources
Software (GitLab Repository)
Data (SustDataED Public Dataset)