Urban Energy Data Visualization and Management: Evaluating Community-Scale Eco-Feedback Approaches

Xu, Lei; Francisco, Abigail; Taylor, John E.

doi:10.1061/(asce)me.1943-5479.0000879

Cited by 14 publications

(9 citation statements)

References 75 publications

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“…Filipova, energy efficiency has re-enforced the benefits of behaviour change to energy consumers in terms of costs and emissions (Staddon et al, 2016). The development of technology is making it easier to inform behaviour at a community scale using an Eco-Feedback approach (Xu et al, 2021) New approaches focused on co-operation and co-creation can support mutual and lasting change, strengthen relationships amongst stakeholders within local policy ecosystems and enhance opportunities for tangible, evidence-based policy solutions that could have an important and positive impact on local communities and the entire society. For example, White, Habib and Hardisty (2019) have suggested that the use of Social influence, Habit formation, Individual self, Feelings & Cognition and Tangibility (referred to as SHIFT) can be a useful framework for influencing consumer behaviour towards sustainable consumption.…”

Section: Communities Of Practice (Cops)mentioning

confidence: 99%

Integrating Sustainability-Oriented Ecologies of Practice Across the Learning Cycle: Supporting Transformative Behaviours in Transgenerational, Transnational and Transdisciplinary Spaces

Adefila

Chen

Dang

et al. 2021

Discourse and Communication for Sustainable Education

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The article examines the complexities associated with effectively and comprehensively tackling the climate change crisis. Focusing on the need for education, the authors discuss a model of Education for Sustainable Development (ESD) that supports the development of competencies, coalition building and the capacity to support and maintain positive action. Drawing upon principles highlighted by the United Nations, the paper outlines the breadth and depth of knowledge required to support transformative ESD. Firstly enhancing comprehensive knowledge that develops cognitive, affective and axiological dimensions and proficiency. This enhances critical engagement with information and enables individuals to act responsibly and align with others in coalition building. The second element refers to collaborative partnership that is crucial for changes to be effective. This has been one of the most challenging barriers preventing positive action on the catastrophe pf climate change. Finally, the paper emphasizes the need to develop the competencies for supporting collective action, which will enable sustained action across transnational, transdisciplinary and transnational boundaries.

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Section: Communities Of Practice (Cops)mentioning

confidence: 99%

Integrating Sustainability-Oriented Ecologies of Practice Across the Learning Cycle: Supporting Transformative Behaviours in Transgenerational, Transnational and Transdisciplinary Spaces

Adefila

Chen

Dang

et al. 2021

Discourse and Communication for Sustainable Education

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“…Particularly, energy efficiency applications employ data analytics to obtain useful insights on power usage patterns. Moreover, sustainable behavioral adjustments have been further motivated by employing eco-feedback systems for less knowledgeable occupants [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Gramian Angular Field classification for edge internet of energy applications

Alsalemi

Amira²,

Malekmohamadi³

et al. 2022

Cluster Comput

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With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization. In this direction, a creative visual approach to understanding energy data is introduced. Building upon micro-moments, which are timeseries of small contextual data points, the power of pictorial representations to encapsulate rich information in a small two-dimensional (2D) space is harnessed through a novel Gramian Angular Fields (GAF) classifier for energy micro-moments. Designed with edge computing efficiency in mind, current testing results on the ODROID-XU4 can classify up to 7 million GAF-converted datapoints with ~ 90% accuracy in less than 30 s, paving the path towards industrial adoption of edge IoE. Supplementary Information The online version contains supplementary material available at 10.1007/s10586-022-03704-1.

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“…For instance, eco-feedback systems have been extensively adopted with the aim of encouraging sustainable behavioral adjustments on the premise that occupants are ignorant of the energy used as a result of their day-to-day activities [3].…”

Section: Introductionmentioning

confidence: 99%

“…Compellingly, data visualization can be a powerful tool to understand that programmatically. Therefore, Machine Learning (ML) can be used hand in hand what is specific data visualizations to yield more useful results and insights [2], [3]. Also, data visualization can be a tool to convert 1-Dimensional (1D) data and to higher dimensional realms that can enable using more complex ML algorithms suitable for multidimensional data.…”

Section: Introductionmentioning

confidence: 99%

Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations

Alsalemi¹,

Amira²,

Malekmohamadi³

et al. 2022

Preprint

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Urban Energy Data Visualization and Management: Evaluating Community-Scale Eco-Feedback Approaches

Cited by 14 publications

References 75 publications

Integrating Sustainability-Oriented Ecologies of Practice Across the Learning Cycle: Supporting Transformative Behaviours in Transgenerational, Transnational and Transdisciplinary Spaces

Integrating Sustainability-Oriented Ecologies of Practice Across the Learning Cycle: Supporting Transformative Behaviours in Transgenerational, Transnational and Transdisciplinary Spaces

Lightweight Gramian Angular Field classification for edge internet of energy applications

Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations

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