Lucas Pereira

The design of a hardware-software platform for long-term energy eco-feedback research

Researchers often face engineering problems, such as optimizing prototype costs and ensuring easy access to the collected data, which are not directly related to the research problems being studied. This is especially true when dealing with long-term studies in real world scenarios. This paper describes the engineering perspective of the design, development and deployment of a long-term real word study on energy eco-feedback, where a non-intrusive home energy monitor was deployed in 30 houses for 18 months. Here we report on the efforts required to implement a cost-effective non-intrusive energy monitor and, in particular, the construction of a local network to allow remote access to multiple monitors and the creation of a RESTful web-service to enable the integration of these monitors with social media and mobile software applications. We conclude with initial results from a few eco-feedback studies that were performed using this platform.

Understanding the Limitations of Eco-feedback: A One-Year Long-Term Study

For the last couple of decades the world has been witnessing a change in habits of energy consumption in domestic environments, with electricity emerging as the main source of energy consumed. The effects of these changes in our eco-system are hard to assess, therefore encouraging researchers from different fields to conduct studies with the goal of understanding and improving perceptions and behaviors regarding household energy consumption. While several of these studies report success in increasing awareness, most of them are limited to short periods of time, thus resulting in a reduced knowledge of how householders will behave in the long-term. In this paper we attempt to reduce this gap presenting a long-term study on household electricity consumption. We deployed a real-time non-intrusive energy monitoring and eco-feedback system in 12 families during 52 weeks. Results show an increased awareness regarding electricity consumption despite a significant decrease in interactions with the eco-feedback system over time. We conclude that after one year of deployment of eco-feedback it was not possible to see any significant increase or decrease in the household consumption. Our results also confirm that consumption is tightly coupled with independent variables like the household size and the income-level of the families.

SustData: A Public Dataset for ICT4S Electric Energy Research

Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy eco- feedback and demand side management research. Index Terms—Public dataset, sustainability, electric energy, feedback.

WATTSBurning: Design and Evaluation of an Innovative Eco-Feedback System

This paper reports a 15 weeks study of artistic eco-feedback deployed in six houses with an innovative sensing infrastructure and visualization strategy. The paper builds on previous work that showed a significant decrease in user awareness after a short period with a relapse in consumption. In this study we aimed to investigate if new forms of feedback could overcome this issue, maintaining the users awareness for longer periods of time. The study presented here aims at understanding if people are more aware of their energy consumption after the installation of a new, art inspired eco-feedback. The research question was then: does artistic eco-feedback provide an increased awareness over normal informative feedback? And does that awareness last longer? To answer this questions participants were interviewed and their consumption patterns analyzed. The main contribution of the paper is to advance our knowledge about the effectiveness of eco-feedback and provide guidelines for implementation of novel eco-feedback visualizations that overcome the relapse behavior pattern.

Show Me or Tell Me: Designing Avatars for Feedback

Avatarscanbeemployedasamotivationaltool,forexample,allowingnon-verbalcommunicationthat can be close to human communication. We describe two lab studies where we presented participants with avatars that communicated verbally via text and visually via expressions. In the first study, participants rated five different categories of captions and corresponding avatars. Results showed that the most persuasive, consistent and trustworthy verbal feedback was given in a humanized form. The second study was an exhaustive forced choice experiment where participants chose the happiest avatar from a pair displayed. Results showed participants found visual avatars more expressive and easier to understand than their verbal counterparts, and that users respond differently when presented with negative or positive emotions. This paper contributes to a better understanding of how to design feedback for expressive avatars.

Semi-automatic labeling for public non-intrusive load monitoring datasets

In this paper we present and evaluate a semiautomatic labeling prototype to enable the creation of fully labeled energy disaggregation datasets from sub-metered data. Our results advocate in favor of our approach and show that it is possible to extract individual appliance transitions with considerable precision, as long as the individual appliance information is present in the sub-metered data, and its resolution is high enough.

Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools-A review

Non‐intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large‐scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross‐datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

What-a-Watt: exploring electricity production literacy through a long term eco-feedback study

This paper presents the design, implementation and evaluation of an eco-feedback system capable of providing detailed household consumption information and also real-time production breakdown per energy source. We build on recent studies reporting an increased awareness generated by ecofeedback systems that also integrate micro-production information, taking advantage of a closed grid production network on an island with a high concentration of renewables, we deployed the What-a-Watt system in a building with 9 households for a period of 34 consecutive weeks. Results show that all the participating families have shown increased awareness of the production and distribution of electricity, thus becoming more familiarized with concepts such as the different sources of energy and how their availability relates to external variables such as weather conditions and time of day. Furthermore, our results also show, that the families using our system have managed to reduce their overall consumption. This research is a first attempt to provide more effective eco-feedback systems to consumers by integrating complex Smartgrid information in the feedback.

WattsBurning on My Mailbox: A Tangible Art Inspired Eco-feedback Visualization for Sharing Energy Consumption

This paper describes a novel art-inspired tangible eco-feedback system. The concept emerged from a workshop with researchers, designers and artists looking at innovative ways to provide more effective eco-feedback that engages users emotionally. The tangible aspect of the system is composed of a set of magnets that users can stick on their physical mailbox outside of their apartment building according to their average energy consumption. The magnets are a total of seven pieces, one for each day of the week. Each piece has a variation of three colors, from green (low consumption) to burning red (high consumption). The magnets are to be displayed in a sequence that represents a typical panorama of local nature. In this paper we report the design and the study we conducted to gauge preliminary results on the system usage and potential. Interviews with participants revealed that none of them felt uncomfortable having their consumption displayed outside. When children were involved in the process they “took control” of the task and pressured their families to perform better.

HomeTree --- an art inspired mobile eco-feedback visualization

This paper presents HomeTree a prototype of an art-inspired mobile eco-feedback system. The system is implemented on a tablet PC and relies on a non-intrusive energy-monitoring infrastructure to access consumption and power event information. Our prototype addresses an important problem in eco-feedback, which is the fact that users loose interest about their energy consumption after a period of several weeks. To accomplish this HomeTree implements a dual visualization strategy. Initially HomeTree presents users with a screensaver that shows energy consumption mapped in a dynamic illustration of the local forest. Through this strategy we leverage the emotional connection between the short-term energy consumption and the long-term effects on nature through the local depicted landscape. In a second mode of operation users can interact with HomeTree directly by checking the historical records of their consumption data, and check which days or weeks they have reduced or increased consumption. Furthermore a comparison with a more objective baseline, such as the city of Funchal energy consumption is provided, in order to give users a sense of the level of their consumption in a wider context.