short-paper

EDA Scale - Assessing Awareness for Energy Dynamics

Authors:

Markus Gödker,

Thomas FrankeAuthors Info & Claims

MuC '19: Proceedings of Mensch und Computer 2019

Pages 683 - 687

https://doi.org/10.1145/3340764.3344891

Published: 08 September 2019 Publication History

Abstract

Energy-related behavior in resource dependent systems (e.g. electric vehicles, houses, ships) is an important factor for the actual energy efficiency such systems can achieve. Energy feedback human-machine-interfaces (HMIs) can support the awareness of energy dynamics in order to comprehend the situation and the influences on energy consumption and to control the system to enhance energy efficiency. To evaluate these HMIs, a scale to assess the energy dynamics awareness (EDA) is needed. Therefore, we developed the EDA scale with 7 items and tested the scale in a study with N = 40 battery electric bus (BEB) drivers on two different HMIs. First results indicated an excellent reliability (Cronbach's ą = .92 and .93) and principal factor analyses revealed a solid single factor structure (61.8% and 67.8% explained variance) with high factor loadings (> .61 for all items). Future studies should further explore the construct and criterion validity of the scale.

References

[1]

Shahzeen Z. Attari, Michael L. DeKay, Cliff I. Davidson, and Wändi Bruine De Bruin. 2010. Public perceptions of energy consumption and savings. Proceedings of the National Academy of sciences 107, 37 (2010), 16054--16059.

[2]

Chris Bingham, Chris Walsh, and Steve Carroll. 2012. Impact of driving characteristics on electric vehicle energy consumption and range. IET Intelligent Transport Systems 6, 1 (2012), 29--35.

[3]

Dirk Brounen, Nils Kok, and John M. Quigley. 2013. Energy literacy, awareness, and conservation behavior of residential households. Energy Economics 38 (2013), 42--50.

[4]

Anna B. Costello and Jason W. Osborne. 2005. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation 10 (2005). Issue 7. http://pareonline.net/getvn.asp?v=10&n=7

[5]

Barry Cripps. 2017. Psychometric testing: Critical perspectives. John Wiley & Sons.

[6]

Sarah Darby. 2006. The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays 486, 2006 (2006), 26.

[7]

Jan E. De Waters and Susan E. Powers. 2011. Energy literacy of secondary students in New York State (USA): A measure of knowledge, affect, and behavior. Energy policy 39, 3 (2011), 1699--1710.

[8]

Joost C.F. De Winter and Dimitra Dodou. 2012. Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics 39, 4 (2012), 695--710.

[9]

Mica R. Endsley. 1988. Situation awareness global assessment technique (SAGAT). In Proceedings of the IEEE 1988 National Aerospace and Electronics Conference. IEEE, 789--795.

[10]

Mica R. Endsley. 1995. Measurement of situation awareness in dynamic systems. Human factors 37, 1 (1995), 65--84.

[11]

Mica R. Endsley. 1995. Toward a theory of situation awareness in dynamic systems. Human factors 37, 1 (1995), 32--64.

[12]

Markus Gödker and Thomas Franke. 2018. Supporting user interaction with the range of electric buses in local public transport. In Poster session presented at the Annual Conference of the Human Factors and Ergonomics Society Europe Chapter, Berlin, DE.

[13]

Anton Gustafsson and Magnus Gyllenswärd. 2005. The power-aware cord: energy awareness through ambient information display. In CHI'05 extended abstracts on Human factors in computing systems. ACM, 1423--1426.

Digital Library

[14]

Melanie R. Herrmann, Duncan P. Brumby, Tadj Oreszczyn, and Xavier M.P. Gilbert. 2018. Does data visualization affect users' understanding of electricity consumption? Building Research & Information 46, 3 (2018), 238--250.

[15]

John L. Horn. 1965. A rationale and test for the number of factors in factor analysis. Psychometrika 30, 2 (01 Jun 1965), 179--185.

[16]

The jamovi project. 2019. Jamovi (Version 1.0){Computer software}. (2019). https://www.jamovi.org/

[17]

Jack Kelly and William J. Knottenbelt. 2016. Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature. CoRR abs/1605.00962 (2016). arXiv:1605.00962 http://arxiv.org/abs/1605.00962

[18]

Hanna Barbara Rasmussen, Marie Lützen, and Signe Jensen. 2018. Energy efficiency at sea: Knowledge, communication, and situational awareness at offshore oil supply and wind turbine vessels. Energy research & social science 44 (2018), 50--60.

[19]

R. M. Taylor. 1990. Development of the Situational Awareness Rating Technique (SART) as a tool for aircrew systems design. In AGARD Conference Proceedings, CP478, Copenhagen, DK.

Cited By

Gödker MMoll VFranke T(2024)Energy Consumption Displays in Electric Vehicles: Differential Effects on Estimating Consumption and Experienced Energy Dynamics AwarenessHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/00187208231222154Online publication date: 9-Jan-2024
https://doi.org/10.1177/00187208231222154
Stahl JGödker MFranke T(2020)Range InSightHCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design10.1007/978-3-030-50523-3_28(393-403)Online publication date: 10-Jul-2020
https://doi.org/10.1007/978-3-030-50523-3_28

Index Terms

EDA Scale - Assessing Awareness for Energy Dynamics
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

Recommendations

Energy consumption awareness in manufacturing and production systems
Factories of the Future: Challenges and Leading Innovations in Intelligent Manufacturing

Industry is a major energy consumer and therefore, manufacturers are continuously trying to reduce manufacturing costs. The improvement of energy usage in manufacturing has been receiving large attention during the last years due to the increasing ...
Toward Improving Building User Energy Awareness
e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Raising awareness of users' energy consumption in non-residential buildings like offices or universities is an essential step toward a global decrease of energy use. A popular solution is to have a visual dashboard with general information about energy ...
Priority-Based Scheduling for Large-Scale Distribute Systems with Energy Awareness
DASC '11: Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Large-scale distributed computing systems (LDSs), such as grids and clouds are primarily designed to provide massive computing capacity. These systems dissipate often excessive energy to both power and cool them. Concerns over greening these systems ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MuC '19: Proceedings of Mensch und Computer 2019

September 2019

863 pages

ISBN:9781450371988

DOI:10.1145/3340764

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

MuC'19

MuC'19: Mensch-und-Computer

September 8 - 11, 2019

Hamburg, Germany

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
46
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gödker MMoll VFranke T(2024)Energy Consumption Displays in Electric Vehicles: Differential Effects on Estimating Consumption and Experienced Energy Dynamics AwarenessHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/00187208231222154Online publication date: 9-Jan-2024
https://doi.org/10.1177/00187208231222154
Stahl JGödker MFranke T(2020)Range InSightHCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design10.1007/978-3-030-50523-3_28(393-403)Online publication date: 10-Jul-2020
https://doi.org/10.1007/978-3-030-50523-3_28

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten